Global Conferences on Artificial Intelligence and Robotics

NEXT EVENT SESSION
07-08 DEC 2023 ( E-Certificate)
For Enquiries:
ai@sciencefather.com

Next Webinar Conference Session starts in

About the Conference

Introduction of the conferences

Artificial Intelligence conferences organized by ScienceFather group. The Global Conferences on Artificial Intelligence and Robotics is a series of conferences that focuses on the latest developments and advancements in the field of artificial intelligence and robotics. These conferences bring together researchers, experts, practitioners, and industry professionals from all around the world to share their knowledge and expertise, discuss challenges and opportunities, and explore emerging trends and technologies in Artificial Intelligence and robotics. The platform for researchers and professionals to present their research findings, exchange ideas, and collaborate on new projects. The conference covers a wide range of topics related to Artificial Intelligence and robotics, including machine learning, computer vision, natural language processing, human-robot interaction, autonomous systems, and many more. The provides an opportunity for participants to network with peers, meet with industry leaders, and learn from experts in the field. It also features keynote speeches, panel discussions, workshops, and exhibitions showcasing the latest innovations and technologies in Artificial Intelligence and robotics. Overall, the Global Conferences on Artificial Intelligence and Robotics provide a unique and valuable opportunity for anyone interested in the field to stay up-to-date with the latest research, trends, and applications of Artificial Intelligence and robotics.
Theme: Exploring the Recent research and Advancements in Artificial Intelligence and Robotics

Objective

Objective

  • Developing intelligent algorithms and ways for problem working, decision timber, and pattern recognition.
  • perfecting the performance and effectiveness of robotics systems, including the design, control, and operation of robots.
  • Advancing the state of computer vision and image processing, including object recognition, image analysis, and image conflation.
  • perfecting the capability of machines to reuse, understand, and respond to natural language.
  • Developing operations of Artificial Intelligence and robotics in disciplines similar as healthcare, finance, transportation, and others.
  • probing the ethical and social counteraccusations of Artificial Intelligence and robotics, and addressing their impact on society and the frugalit
  • Developing new Artificial Intelligence and robotics technologies to support artificial robotization and enhance productivity in manufacturing and assiduity.
  • perfecting the security and sequestration of Artificial Intelligence and robotics systems, including the use of Artificial Intelligence in cybersecurity.

Organizers

Organizers

Science Father is a international conferences  organizer and publish the videos, books and news in various themes of scientific research. Articles Presented in our conference are Peer Reviewed. We build the perfect environment for learning, sharing, networking and Awarding via Academic conferences, workshops, symposiums, seminars, awards and other events. We establish our Relationship with the scholars and the Universities through various activities such as seminars, workshops, conferences and Symposia. We are a decisive, conclusive & fast-moving company open to new ideas and ingenious publishing. We also preserve the long-term relationships with our authors and supporting them throughout their careers. We acquire, develop and distribute knowledge by disseminating scholarly and professional materials around the world. All  conference and award presentations are maintain the highest standards of quality, with Editorial Boards composed of scholars & Experts from around the world.

Date and Location

Date and Location

Global Conferences on Artificial Intelligence and Robotics, Organized by ScienceFather group

11th Edition of Artificial Intelligence and Robotics |24-25 September 2023 | Mumbai, India

12th Edition of Artificial Intelligence and Robotics |19-20 October 2023 | Paris, France

13th Edition of Artificial Intelligence and Robotics |26-27 November 2023 | Agra,  India

14th Edition of Artificial Intelligence and Robotics |07-08 December 2023 | Dubai, United Arab Emirates

Call for Paper

Call for paper

Original Articles/papers are invited from Industry Persons, Scientist, Academician, Research Scholars, P.G. & U.G. Students for presentation in our International Conference. All articles/papers must be in MS-Word (.doc or .docx) format, including the title, author's name, an affiliation of all authors, e-mail, abstract, keywords, Conclusion, Acknowledgment, and References.

Submit Abstract

The Candidates with eligibility can click the "Submit Paper/Abstract Now" button and fill up the online submission form and Submit.

Abstract/Full Paper submission

Final/Full Paper submission is optional: If you don't want your abstract/full paper to be published in the Conference Abstracts & Proceedings CD (with ISBN number) and only want to present it at the conference, it is acceptable.

Page limit: There is a limit of 6-8 pages for a final/full paper. An additional page is chargeable.

Paper language: Final/Full papers should be in English.

Templates: "Final paper template," "Final abstract template"

All the final papers should be uploaded to the website online system according to "The final paper template" as word doc. Or Docx, since this will be the camera-ready published version. Please note that final papers that are not uploaded to online System as a word doc./docx after the opening of final paper submissions according to the template above will not be published in the CONFERENCE Abstracts & Proceedings CD (with ISBN

Journal Publication

Journal Publication

Artificial Intelligence Conferences All accepted papers will be included in the conference proceedings, which will be recommended in one of the author's prescribed ScienceFather International journals.

Registration Procedure

Registration Procedure

Click the “Register Now” button on the conference page and enter your Submission ID in the Search Box

  • Your Submissions will be listed on that page. You can find the Register Now link beside your submission. Click the link, and now you will be redirected to the Conference registration form where you can make your registration using credit/debit cards.
  • The Fee charged for E-Poster is to display the E-Posters only on the Website. The Abstract will be published in the conference proceeding book.

Registration Types

Speaker Registration

  • Access to all event Session
  • Certificate of Presentation
  • Handbook
  • Conference Kit
  • Tea, Coffee & Snack,
  • Lunch during the Conference
  • Publication of Abstract /Full Paper at the Conference Proceedings Book
  • Opportunity to give a Keynote/ Poster Presentations/ Plenary/ Workshop
  • Opportunity to publish your Abstract in any of our esteemed Journals discounted rate
  • Opportunity to publish your full article in our open access book at a discounted rate
  • One to One Expert Forums

Delegate (Participant) Registration

  • Access to all Event Sessions
  • Participation Certificate
  • Handbook
  • Conference Kit
  • Tea, Coffee & Snack,
  • Lunch during the Conference
  • Delegates are not allowed to present

Poster Registration

  • Includes all the above Registration Benefits
  • You will have to bring your Posters to the Conference Venue
  • Best poster award memento and certificate on stage.

Poster Guidelines

  • The poster should be 1×1 m Size.
  • The title, contents, text, and the author’s information should be visible.
  • Present numerical data in the form of graphs rather than tables.
  • Figures make trends in the data much more evident.
  • Avoid submitting high word-count posters.
  • Poster contains, e.g., Introduction, Methods, Results, Discussion, Conclusions, and Literature.

Research Forum (Awards)

  • Includes all the above Registration Benefits.
  • The attendee should be required age limit.
  • Award memento and certificate on stage.

E-Poster Presentation

  • The amount charged for E-Posters is to display the E-Posters only on the website
  • The presenter will get an e-poster participation certificate as a soft copy
  • The abstract will be published in the particular journal and also in the conference proceeding book
  • The presenter is not required to be present in person at the Conference

Video Presentation

  • The amount charged for Video Presentation is to display the Presentation at the Conference.
  • The presenter will get Video participation certificate as a soft copy
  • The abstract will be published in the particular journal and also in the conference proceeding book
  • The presenter is not required to be present in person at the Conference

Accompanying Person

    • Accompanying Persons attend the participants at the Conference who may be either a spouse/family partner or a son/daughter and must register under this category.
    • Please note that business partners do not qualify as Accompanying Persons and cannot register as an Accompanying Person.

Committee Members

List of Committee Members

TitleFirst NameLast NameInstitution/OrganizationCountry
Assist Prof DrKrishna KumarMohbeyCentral University of RajasthanIndia
DrAli OthmanAlbajiFaculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru, MalaysiaMalaysia
DrFaridSamsami KhodadadAmol University of Special Modern TechnologiesIran
DrRameshMSRM Institute of Science and HumanitiesIndia
Prof DrDesikanRameshTamil Nadu Agricultural UniversityIndia
DrBhavnaBajpaiDr C V Raman University, KhandwaIndia
DrIvanIzoninLviv Polytechnic National UniversityUkraine
Assoc Prof Drismetmeydanyüzüncü yil universityTurkey
Assoc Prof DrNasrinLotfiSchool of Advanced Technologies in MedicineIran
DrChinmayaSahuVellore Institute of Technology VelloreIndia
MrMuhammad UsmanAsadDalhousie UniversityCanada
DrShanmugasundarGSri Sai Ram Institute of TechnologyIndia
DrSrikarAnnamrajuUniversity of Illinois Urbana ChampaignIndia
DrSaravana MohanMKumaraguru College of TechnologyIndia
TitleFirst NameLast NameInstitution/OrganizationCountry

Conference Awards

Details of Conference Awards

Sciencefather awards Researchers and Research organizations around the world with the motive of Encouraging and Honoring them for their Significant contributions & Achievements for the Advancement in their field of expertise. Researchers and scholars of all nationalities are eligible to receive Sciencefather Research awards. Nominees are judged on past accomplishments, research excellence, and outstanding academic achievements.

Award Categories

Best Poster Award

Posters will be evaluated based on Presentation Style, Research Quality, and Layout/Design. Unique opportunity to combine visual and oral explanations of your projects in the form of poster presentation. Posters should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. The size of the poster should be: 1mX1.5m; Text:16-26 pt; Headings: 32-50 pt; Title: 70 pt; Color: Preferable. Bring your poster to the meeting, using tubular packaging and presenting duration: 10 min discussion & 5 min query per person. Eligibility: The presenter can nominate the Award. He must be under 40 years of age as on the conference date.

Best Presentation Award

The presentation will be evaluated based on Presentation Style, Research Quality, and Layout/Design. Unique opportunity to combine visual and oral explanations of your projects in the form of poster presentations. The presentation should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. Bring your presentation to the meeting, using a pen drive, presenting duration: 10-20 min discussion & 5 min query per person. Eligibility: The presenter can nominate the Award. He must be under 55 years of age as of the conference date.

Best Paper Award

Paper will be evaluated based on Format, Research Quality, and Layout/Design. The paper should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. Eligibility: The presenter can nominate the Award. He must be under 55 years of age as of the conference date.

Instructions

Instructions for submission

If you want to submit only your Abstract

  • If you want to publish only your abstract (it is also optional) in the CONFERENCE Abstracts & Proceedings CD (with ISBN), upload your abstract again according to the Final abstract template as a word doc. Or Docx.
  • If you also don't want your abstract to be published in the CONFERENCE Abstracts & Proceedings CD (with an ISBN) and only want to present it at the conference, it is also acceptable.

How to Submit your Abstract / Full Paper

Please read the instructions below then submit your Abstract/ Full Paper (or just final abstract) via the online conference system:

  • STEP 1: Please download the Abstract /Final Paper Template and submit your final paper strictly according to the template: Artificial Intelligence Conference Final Paper Template in word format (.doc /.docx). See a Final abstract template formatted according to the template.
  • STEP 2: Please ensure that the Abstract/ full paper follows exactly the format and template described in the final paper template document below since this will be the camera-ready published version. All last articles should be written only in English and "word document" as .doc or .docx.
  • STEP 3: You can submit your final paper(s) to the online conference system only by uploading/ Re-submission your current submission.
  • STEP 4: After logging/using submission ID in the online conference system, click on the "Re-submission" link at the bottom of the page.
  • STEP 5: After the "Re submission page" opens, upload your abstract/ final paper (it should be MS word document -doc. or Docx-).

General Information

  • Dress Code: Participants have to wear a formal dress. There are no restrictions on color or design. The audience attending only the ceremony can wear clothing of their own choice.
  • Certificate Distribution: Each presenter's name will be called & asked to collect their certificate on the Stage with an official photographer to capture the moments.

Terms & Conditions

Terms & Conditions

Artificial Intelligence Conferences Terms & Conditions Policy was last updated on June 25, 2022.

Privacy Policy

Artificial Intelligence conferences customer personal information for our legitimate business purposes, process and respond to inquiries, and provide our services, to manage our relationship with editors, authors, institutional clients, service providers, and other business contacts, to market our services and subscription management. We do not sell, rent/ trade your personal information to third parties.

Relationship

Artificial Intelligence Conferences Operates a Customer Association Management and email list program, which we use to inform customers and other contacts about our services, including our publications and events. Such marketing messages may contain tracking technologies to track subscriber activity relating to engagement, demographics, and other data and build subscriber profiles.

Disclaimer

All editorial matter published on this website represents the authors' opinions and not necessarily those of the Publisher with the publications. Statements and opinions expressed do not represent the official policies of the relevant Associations unless so stated. Every effort has been made to ensure the accuracy of the material that appears on this website. Please ignore, however, that some errors may occur.

Responsibility

Delegates are personally responsible for their belongings at the venue. The Organizers will not be held accountable for any stolen or missing items belonging to Delegates, Speakers, or Attendees; due to any reason whatsoever.

Insurance

Artificial Intelligence conferences Registration fees do not include insurance of any kind.

Press and Media

Press permission must be obtained from the Artificial Intelligence conferences Organizing Committee before the event. The press will not quote speakers or delegates unless they have obtained their approval in writing. This conference is not associated with any commercial meeting company.

Transportation

Artificial Intelligence Conferences Please note that any (or) all traffic and parking is the registrant's responsibility.

Requesting an Invitation Letter

Artificial Intelligence Conferences For security purposes, the invitation letter will be sent only to those who had registered for the conference. Once your registration is complete, please contact ai@ScienceFather.com to request a personalized letter of invitation.

Cancellation Policy

If Artificial Intelligence Conferences cancels this event, you will receive a credit for 100% of the registration fee paid. You may use this credit for another Artificial Intelligence Conferences event, which must occur within one year from the cancellation date.

Postponement Policy

Suppose Artificial Intelligence Conferences postpones an event for any reason and you are unable or indisposed to attend on rescheduled dates. In that case, you will receive a credit for 100% of the registration fee paid. You may use this credit for another Artificial Intelligence Conferences, which must occur within one year from the date of postponement.

Transfer of registration

Artificial Intelligence Conferences All fully paid registrations are transferable to other persons from the same organization if the registered person is unable to attend the event. The registered person must make transfers in writing to ai@sciencefather.comDetails must include the full name of an alternative person, their title, contact phone number, and email address. All other registration details will be assigned to the new person unless otherwise specified. Registration can be transferred to one conference to another conference of ScienceFather if the person cannot attend one of the meetings. However, Registration cannot be transferred if it will be intimated within 14 days of the particular conference. The transferred registrations will not be eligible for Refund.

Visa Information

Artificial Intelligence Conferences Keeping given increased security measures, we would like to request all the participants to apply for Visa as soon as possible. ScienceFather will not directly contact embassies and consulates on behalf of visa applicants. All delegates or invitees should apply for Business Visa only. Important note for failed visa applications: Visa issues cannot come under the consideration of the cancellation policy of ScienceFather, including the inability to obtain a visa.

Refund Policy

Artificial Intelligence Conferences Regarding refunds, all bank charges will be for the registrant's account. All cancellations or modifications of registration must make in writing to air@sciencefather.com

If the registrant is unable to attend and is not in a position to transfer his/her participation to another person or event, then the following refund arrangements apply:

Keeping given advance payments towards Venue, Printing, Shipping, Hotels and other overheads, we had to keep Refund Policy is as following conditions,

  • Before 60 days of the Conference: Eligible for Full Refund less $100 Service Fee
  • Within 60-30 days of Conference: Eligible for 50% of payment Refund
  • Within 30 days of Conference: Not eligible for Refund
  • E-Poster Payments will not be refunded.

Accommodation Cancellation Policy

Artificial Intelligence Conferences Accommodation Providers such as hotels have their cancellation policies, and they generally apply when cancellations are made less than 30 days before arrival. Please contact us as soon as possible if you wish to cancel or amend your accommodation. ScienceFather will advise your accommodation provider's cancellation policy before withdrawing or changing your booking to ensure you are fully aware of any non-refundable deposits.

Related Journals

.table-wrap {
  height: 80px;
  overflow-y: auto;
}

Related Journals

1. IEEE Transactions on Neural Networks and Learning Systems (Scopus, SCI, Impact Factor: 11.683, Publisher: IEEE, Year of First Issue: 1990) | 2. IEEE Transactions on Pattern Analysis and Machine Intelligence (Scopus, SCI, Impact Factor: 9.025, Publisher: IEEE, Year of First Issue: 1979) | 3. Machine Learning (Scopus, SCI, Impact Factor: 4.636, Publisher: Springer, Year of First Issue: 1986) | 4. Journal of Machine Learning Research (Scopus, SCI, Impact Factor: 4.147, Publisher: JMLR, Year of First Issue: 2000) | 5. Journal of Artificial Intelligence Research (Scopus, SCI, Impact Factor: 3.576, Publisher: Artificial Intelligence Access Foundation, Year of First Issue: 1993) | 6. ACM Transactions on Intelligent Systems and Technology (Scopus, SCI, Impact Factor: 3.431, Publisher: ACM, Year of First Issue: 2010) | 7. IEEE Transactions on Fuzzy Systems (Scopus, SCI, Impact Factor: 8.746, Publisher: IEEE, Year of First Issue: 1993) | 8. Information Sciences (Scopus, SCI, Impact Factor: 5.910, Publisher: Elsevier, Year of First Issue: 1968) | 9. Neural Networks (Scopus, SCI, Impact Factor: 4.797, Publisher: Elsevier, Year of First Issue: 1988) | 10. Journal of the American Medical Informatics Association (Scopus, SCI, Impact Factor: 5.223, Publisher: Oxford University Press, Year of First Issue: 1994) | 11. IEEE Transactions on Evolutionary Computation (Scopus, SCI, Impact Factor: 8.759, Publisher: IEEE, Year of First Issue: 1997) | 12. IEEE Transactions on Cybernetics (Scopus, SCI, Impact Factor: 10.387, Publisher: IEEE, Year of First Issue: 1971) | 13. Artificial Intelligence (Scopus, SCI, Impact Factor: 7.420, Publisher: Elsevier, Year of First Issue: 1970) | 14. IEEE Transactions on Systems, Man, and Cybernetics (Scopus, SCI, Impact Factor: 10.662, Publisher: IEEE, Year of First Issue: 1971) | 15. IEEE Transactions on Knowledge and Data Engineering (Scopus, SCI, Impact Factor: 5.595, Publisher: IEEE, Year of First Issue: 1989) | 16. Pattern Recognition (Scopus, SCI, Impact Factor: 6.389, Publisher: Elsevier, Year of First Issue: 1968) | 17. International Journal of Computer Vision (Scopus, SCI, Impact Factor: 11.507, Publisher: Springer, Year of First Issue: 1987) | 18. ACM Transactions on Interactive Intelligent Systems (Scopus, Impact Factor: 1.739, Publisher: ACM, Year of First Issue: 2011) | 19. Neural Computing and Applications (Scopus, SCI, Impact Factor: 4.774, Publisher: Springer, Year of First Issue: 1992) | 20. Computational Intelligence and Neuroscience (Scopus, SCI, Impact Factor: 2.514, Publisher: Hindawi, Year of First Issue: 2004) | 21. Expert Systems with Applications (Scopus, SCI, Impact Factor: 5.450, Publisher: Elsevier, Year of First Issue: 1988) | 22. IEEE Transactions on Big Data (Scopus, SCI, Impact Factor: 6.929, Publisher: IEEE, Year of First Issue: 2015) | 23. IEEE Transactions on Intelligent Transportation Systems (Scopus, SCI, Impact Factor: 6.434, Publisher: IEEE, Year of First Issue: 2000) | 24. IEEE Transactions on Affective Computing (Scopus, SCI, Impact Factor: 7.239, Publisher: IEEE, Year of First Issue: 2010) | 25. IEEE Transactions on Games (Scopus, SCI, Impact Factor: 3.020, Publisher: IEEE, Year of First Issue: 2009) | 26. IEEE Transactions on Mobile Computing (Scopus, SCI, Impact Factor: 4.574, Publisher: IEEE, Year of First Issue: 2002) | 27. IEEE Transactions on Multimedia (Scopus, SCI, Impact Factor: 6.611, Publisher: IEEE, Year of First Issue: 1999) | 28. IEEE Transactions on Signal Processing (Scopus, SCI, Impact Factor: 10.371, Publisher: IEEE, Year of First Issue: 1952) | 29. Journal of Intelligent Information Systems (Scopus, SCI, Impact Factor: 3.189, Publisher: Springer, Year of First Issue: 1992) | 30. Artificial Intelligence in Medicine (Scopus, SCI, Impact Factor: 4.279, Publisher: Elsevier, Year of First Issue: 1989) | 31. Computer Methods and Programs in Biomedicine (Scopus, SCI, Impact Factor: 3.632, Publisher: Elsevier, Year of First Issue: 1973) | 32. ACM Transactions on Knowledge Discovery from Data (Scopus, SCI, Impact Factor: 3.723, Publisher: ACM, Year of First Issue: 2007) | 33. Frontiers in Robotics and Artificial Intelligence (Scopus, SCI, Impact Factor: 3.792, Publisher: Frontiers Media, Year of First Issue: 2014) | 34. Journal of Robotics and Autonomous Systems (Scopus, SCI, Impact Factor: 5.361, Publisher: Elsevier, Year of First Issue: 1985) | 35. Neurocomputing (Scopus, SCI, Impact Factor: 5.159, Publisher: Elsevier, Year of First Issue: 1989) | 36. Swarm Intelligence (Scopus, SCI, Impact Factor: 3.546, Publisher: Springer, Year of First Issue: 2007) | 37. Journal of Intelligent Manufacturing (Scopus, SCI, Impact Factor: 4.103, Publisher: Springer, Year of First Issue: 1990) | 38. IEEE Intelligent Systems (Scopus, SCI, Impact Factor: 6.763, Publisher: IEEE, Year of First Issue: 1986) | 39. International Journal of Neural Systems (Scopus, SCI, Impact Factor: 4.754, Publisher: World Scientific, Year of First Issue: 1989) | 40. International Journal of Computer Vision (Scopus, SCI, Impact Factor: 11.654, Publisher: Springer, Year of First Issue: 1987) | 41. IEEE Transactions on Pattern Analysis and Machine Intelligence (Scopus, SCI, Impact Factor: 17.734, Publisher: IEEE, Year of First Issue: 1979) | 42. IEEE Transactions on Cybernetics (Scopus, SCI, Impact Factor: 10.387, Publisher: IEEE, Year of First Issue: 1971) | 43. Pattern Recognition (Scopus, SCI, Impact Factor: 8.106, Publisher: Elsevier, Year of First Issue: 1968) | 44. Neural Networks (Scopus, SCI, Impact Factor: 4.950, Publisher: Elsevier, Year of First Issue: 1988) | 45. Knowledge-Based Systems (Scopus, SCI, Impact Factor: 6.038, Publisher: Elsevier, Year of First Issue: 1988) | 46. Journal of Machine Learning Research (Scopus, SCI, Impact Factor: 3.810, Publisher: JMLR, Year of First Issue: 2000) | 47. ACM Transactions on Intelligent Systems and Technology (Scopus, SCI, Impact Factor: 3.617, Publisher: ACM, Year of First Issue: 2010) | 48. Information Fusion (Scopus, SCI, Impact Factor: 10.716, Publisher: Elsevier, Year of First Issue: 2000) | 49. IEEE Transactions on Fuzzy Systems (Scopus, SCI, Impact Factor: 10.104, Publisher: IEEE, Year of First Issue: 1993) | 50. Journal of Artificial Intelligence Research (Scopus, SCI, Impact Factor: 3.647, Publisher: Artificial Intelligence Access, Year of First Issue: 1993) | 51. Evolutionary Computation (Scopus, SCI, Impact Factor: 3.190, Publisher: MIT Press, Year of First Issue: 1993) | 52. IEEE Transactions on Evolutionary Computation (Scopus, SCI, Impact Factor: 8.556, Publisher: IEEE, Year of First Issue: 1997) | 53. Cognitive Computation (Scopus, SCI, Impact Factor: 4.118, Publisher: Springer, Year of First Issue: 2009) | 54. Applied Soft Computing (Scopus, SCI, Impact Factor: 6.039, Publisher: Elsevier, Year of First Issue: 2001) | 55. Autonomous Robots (Scopus, SCI, Impact Factor: 4.340, Publisher: Springer, Year of First Issue: 1994) | 56. IEEE Transactions on Robotics (Scopus, SCI, Impact Factor: 9.425, Publisher: IEEE, Year of First Issue: 1984) | 57. Machine Learning (Scopus, SCI, Impact Factor: 5.959, Publisher: Springer, Year of First Issue: 1986) | 58. Artificial Intelligence Magazine (Scopus, SCI, Impact Factor: 0.652, Publisher: AAArtificial Intelligence, Year of First Issue: 1980) | 59. IEEE Computational Intelligence Magazine (Scopus, SCI, Impact Factor: 9.050, Publisher: IEEE, Year of First Issue: 2006) | 60. Journal of Artificial Intelligence and Soft Computing Research (Scopus, SCI, Impact Factor: 1.802, Publisher: World Scientific, Year of First Issue: 2011) | 61. Journal of Machine Learning Research - Proceedings Track (Scopus, SCI, Publisher: JMLR, Year of First Issue: 2007) | 62. Journal of Intelligent Information Systems (Scopus, SCI, Impact Factor: 2.981, Publisher: Springer, Year of First Issue: 1992) | 63. Swarm and Evolutionary Computation (Scopus, SCI, Impact Factor: 6.145, Publisher: Elsevier, Year of First Issue: 2011) | 64. Artificial Intelligence Review (Scopus, SCI, Impact Factor: 7.059, Publisher: Springer, Year of First Issue: 1987) | 65. IEEE Transactions on Neural Networks and Learning Systems (Scopus, SCI, Impact Factor: 13.207, Publisher: IEEE, Year of First Issue: 1990) | 66. Expert Systems with Applications (Scopus, SCI, Impact Factor: 6.954, Publisher: Elsevier, Year of First Issue: 1988) | 67. Journal of Ambient Intelligence and Humanized Computing (Scopus, SCI, Impact Factor: 3.871, Publisher: Springer, Year of First Issue: 2010) | 68. Artificial Intelligence in Medicine (Scopus, SCI, Impact Factor: 4.212, Publisher: Elsevier, Year of First Issue: 1989) | 69. Neurocomputing (Scopus, SCI, Impact Factor: 4.438, Publisher: Elsevier, Year of First Issue: 1989) | 70. Applied Intelligence (Scopus, SCI, Impact Factor: 2.792, Publisher: Springer, Year of First Issue: 1991) | 71. Swarm Intelligence (Scopus, SCI, Impact Factor: 3.605, Publisher: Springer, Year of First Issue: 2007) | 72. IEEE Transactions on Autonomous Mental Development (Scopus, SCI, Impact Factor: 2.619, Publisher: IEEE, Year of First Issue: 2009) | 73. Journal of Robotics (Scopus, SCI, Publisher: Hindawi, Year of First Issue: 2010) | 74. Journal of Intelligent Systems (Scopus, SCI, Impact Factor: 1.120, Publisher: Taylor & Francis, Year of First Issue: 1991)

Related Conferences

Related Conferences

AAArtificial Intelligence Conference on Artificial Intelligence (AAArtificial Intelligence) | ACM Conference on Computer and Communications Security (CCS) | ACM Conference on Human-Computer Interaction (CHI) | ACM Conference on Information and Knowledge Management (CIKM) | ACM Conference on Recommender Systems (RecSys) | ACM Conference on Web Search and Data Mining (WSDM) | ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) | ACM Symposium on Principles of Distributed Computing (PODC) | ACM Symposium on Theory of Computing (STOC) | Advances in Neural Information Processing Systems (NeurIPS) | Artificial Intelligence for Good Global Summit | Artificial Intelligence Ethics and Society Conference | Artificial Intelligence Frontiers Conference | Artificial Intelligence in Finance Summit | Artificial Intelligence NextCon | Applied Machine Learning Days (AMLD) | Artificial Intelligence and Data Science Conference (Artificial IntelligenceDSC) | Artificial Intelligence Conference | Asia Conference on Machine Learning (ACML) | Association for Computational Linguistics (ACL) | Big Data and Artificial Intelligence Toronto | Bioinformatics and Computational Biology Conference (BCB) | Canadian Conference on Artificial Intelligence (CArtificial IntelligenceAC) | Conference on Computer Vision and Pattern Recognition (CVPR) | Conference on Empirical Methods in Natural Language Processing (EMNLP) | Conference on Information and Communication Technologies and Development (ICTD) | Conference on Machine Learning and Systems (MLSys) | Conference on Neural Information Processing Systems (NeurIPS) | Conference on Robot Learning (CoRL) | Conversational Artificial Intelligence Summit | CVPR Workshop on Autonomous Driving | Data Science Conference | Data Science Conference Europe | Data Science Conference USA | Deep Learning Summit | Deep Learning World | European Conference on Computer Vision (ECCV) | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) | European Conference on Natural Language Processing (EMNLP) | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) | Foundations of Responsible Computing (FORC) | Gartner Data & Analytics Summit | GPU Technology Conference (GTC) | H2O Artificial Intelligence World | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | IEEE Conference on Data Mining (ICDM) | IEEE Conference on Decision and Control (CDC) | IEEE Conference on Robotics and Automation (ICRA) | IEEE International Conference on Big Data (BigData) | IEEE International Conference on Computer Vision (ICCV) | IEEE International Conference on Data Engineering (ICDE) | IEEE International Conference on Robotics and Automation (ICRA) | IEEE International Conference on Tools with Artificial Intelligence (ICTArtificial Intelligence) | IEEE World Congress on Computational Intelligence (WCCI) | IFIP International Conference on Artificial Intelligence in Theory and Practice (IFIP Artificial Intelligence) | Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) | Innovate Artificial Intelligence | International Conference on Learning Representations (ICLR) | International Conference on Machine Learning (ICML) | International Conference on Natural Language and Speech Processing (ICNLSP) | International Conference on Robotics and Automation (ICRA) | International Joint Conference on Artificial Intelligence (IJCArtificial Intelligence) | International Symposium on Artificial Intelligence and Mathematics (ISArtificial IntelligenceM) | International Symposium on Experimental Robotics (ISER) | International Workshop on Machine Learning in Medical Imaging (MLMI) | NeurIPS (Conference on Neural Information Processing Systems) | ICML (International Conference on Machine Learning) | ICLR (International Conference on Learning Representations) | AAArtificial Intelligence (Association for the Advancement of Artificial Intelligence) Conference | CVPR (Computer Vision and Pattern Recognition) | ECCV (European Conference on Computer Vision) | ICCV (International Conference on Computer Vision) | ACL (Association for Computational Linguistics) Conference | EMNLP (Empirical Methods in Natural Language Processing) | COLING (International Conference on Computational Linguistics) | Artificial IntelligenceSTATS (International Conference on Artificial Intelligence and Statistics) | UArtificial Intelligence (Conference on Uncertainty in Artificial Intelligence) | IJCArtificial Intelligence (International Joint Conference on Artificial Intelligence) | KDD (Knowledge Discovery and Data Mining) | WSDM (Web Search and Data Mining) | RecSys (ACM Conference on Recommender Systems) | ECML-PKDD (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases) | ICDM (IEEE International Conference on Data Mining) | CIKM (ACM International Conference on Information and Knowledge Management) | ICWSM (International Conference on Web and Social Media) | ICPR (International Conference on Pattern Recognition) | ICASSP (IEEE International Conference on Acoustics, Speech, and Signal Processing) | ICIP (IEEE International Conference on Image Processing) | NAACL (North American Chapter of the Association for Computational Linguistics) Conference | AMIA (American Medical Informatics Association) Annual Symposium | BIBM (IEEE International Conference on Bioinformatics and Biomedicine) | CEC (IEEE Congress on Evolutionary Computation) | SSCI (IEEE Symposium Series on Computational Intelligence) | IJCNN (International Joint Conference on Neural Networks) | FUZZ-IEEE (IEEE International Conference on Fuzzy Systems) | ICANN (International Conference on Artificial Neural Networks) | AAArtificial Intelligence Spring Symposium Series | ECArtificial Intelligence (European Conference on Artificial Intelligence) | IROS (IEEE/RSJ International Conference on Intelligent Robots and Systems) | ICRA (IEEE International Conference on Robotics and Automation) | AAMAS (International Conference on Autonomous Agents and Multiagent Systems) | RO-MAN (IEEE International Symposium on Robot and Human Interactive Communication) | HRI (ACM/IEEE International Conference on Human-Robot Interaction) | ICALT (IEEE International Conference on Advanced Learning Technologies) | ITS (Intelligent Tutoring Systems) | ICMI (ACM International Conference on Multimodal Interaction) | IUI (International Conference on Intelligent User Interfaces) | UMAP (User Modeling, Adaptation and Personalization) | ICWL (International Conference on Web-Based Learning) | EC-TEL (European Conference on Technology Enhanced Learning) | LAK (International Conference on Learning Analytics and Knowledge) | EDM (International Conference on Educational Data Mining) | Artificial IntelligenceED (Artificial Intelligence in Education) | CogSci (Cognitive Science Society Annual Meeting) | ICDL-EpiRob (IEEE International Conference on Development and Learning and Epigenetic Robotics) | BICA (Annual International Conference on Biologically Inspired Cognitive Architectures) | ICRArtificial Intelligence (International Conference on Robotics and Artificial Intelligence) | IASTED (International Association of Science and Technology for Development) International Conference on Artificial Intelligence and Applications | ICCMA (International Conference on Control, Mechatronics and Automation | Conference on Neural Information Processing Systems (NeurIPS) | International Conference on Machine Learning (ICML) | Conference on Computer Vision and Pattern Recognition (CVPR) | Association for Computational Linguistics (ACL) | International Conference on Learning Representations (ICLR) | International Joint Conference on Artificial Intelligence (IJCArtificial Intelligence) | European Conference on Computer Vision (ECCV) | AAArtificial Intelligence Conference on Artificial Intelligence (AAArtificial Intelligence) | ACM Conference on Computer and Communications Security (CCS) | Conference on Empirical Methods in Natural Language Processing (EMNLP) | International Conference on Computer Vision (ICCV) | IEEE International Conference on Robotics and Automation (ICRA) | International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) | The Web Conference (WWW) | ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) | ACM Conference on Human-Computer Interaction (CHI) | Conference on Computational Linguistics and Intelligent Text Processing (CICLing) | IEEE International Conference on Data Mining (ICDM) | International Conference on Artificial Neural Networks (ICANN) | IEEE International Conference on Robotics and Biomimetics (ROBIO) | ACM Conference on Information and Knowledge Management (CIKM) | ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW) | European Conference on Artificial Intelligence (ECArtificial Intelligence) | ACM Conference on Recommender Systems (RecSys) | Conference on Robot Learning (CoRL) | ACM Conference on Information Retrieval (SIGIR) | International Conference on Intelligent Robots and Systems (IROS) | IEEE Conference on Computer Communications (INFOCOM) | International Conference on Machine Learning and Data Mining (MLDM) | International Conference on Intelligent User Interfaces (IUI) | ACM International Conference on Multimedia (ACMMM) | IEEE International Conference on Systems, Man, and Cybernetics (SMC) | IEEE International Conference on Tools with Artificial Intelligence (ICTArtificial Intelligence) | International Conference on Autonomous Intelligent Systems (IAS) | International Conference on Machine Learning and Applications (ICMLA) | International Conference on Computer and Information Technology (ICCIT) | International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) | International Joint Conference on Natural Language Processing (IJCNLP) | International Conference on Computational Linguistics (COLING) | IEEE Conference on Computational Intelligence and Games (CIG) | ACM Conference on Human Factors in Computing Systems (CHI) | ACM SIGGRAPH Conference on Computer Graphics and Interactive Techniques (SIGGRAPH) | ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) | ACM Symposium on User Interface Software and Technology (UIST) | ACM Conference on Computer Science (CS) | ACM Conference on Information Retrieval (SIGIR) | International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) | International Conference on Automated Planning and Scheduling (ICAPS) | International Conference on Cognitive Computing and Information Processing (CCIP) | International Conference on Data Mining and Knowledge Engineering (ICDMKE) | International Conference on Information Fusion (FUSION) | International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) | International Conference on Natural Language Processing (ICON) | International Conference on Natural Language Processing and Information Retrieval (NLPIR)

Related Societies

Related Societies

1. Association for Computing Machinery (ACM) - United States | 2. IEEE Computational Intelligence Society - United States | 3. AAArtificial Intelligence - Association for the Advancement of Artificial Intelligence - United States | 4. British Computer Society (BCS) - United Kingdom | 5. European Association for Artificial Intelligence (EurArtificial Intelligence) - Europe | 6. Japanese Society for Artificial Intelligence (JSArtificial Intelligence) - Japan | 7. Canadian Artificial Intelligence Association (CArtificial IntelligenceAC) - Canada | 8. Korean Society of Computer Information (KSCI) - South Korea | 9. Chinese Association for Artificial Intelligence (CAArtificial Intelligence) - China | 10. Australian Society for Artificial Intelligence (ASArtificial Intelligence) - Australia | 11. Italian Association for Artificial Intelligence (Artificial IntelligencexIA) - Italy | 12. French Association for Artificial Intelligence (AFIA) - France | 13. German Society for Artificial Intelligence (KI) - Germany | 14. Russian Association for Artificial Intelligence (RAArtificial Intelligence) - Russia | 15. Spanish Association for Artificial Intelligence (AEPIA) - Spain | 16. Swedish Artificial Intelligence Society (SArtificial IntelligenceS) - Sweden | 17. Swiss Artificial Intelligence Society (SGArtificial Intelligence) - Switzerland | 18. Austrian Society for Artificial Intelligence (ÖGArtificial Intelligence) - Austria | 19. Danish Society for Artificial Intelligence (DAArtificial Intelligence) - Denmark | 20. Dutch Association for Artificial Intelligence (NVArtificial Intelligence) - Netherlands | 21. Belgian Association for Artificial Intelligence (BNArtificial IntelligenceC) - Belgium | 22. Finnish Artificial Intelligence Society (FArtificial IntelligenceS) - Finland | 23. Norwegian Artificial Intelligence Society (NArtificial IntelligenceS) - Norway | 24. Portuguese Association for Artificial Intelligence (APPIA) - Portugal | 25. Greek Association for Artificial Intelligence (EETN) - Greece | 26. Brazilian Society of Computing (SBC) - Brazil | 27. Mexican Society for Artificial Intelligence (SMIA) - Mexico | 28. Argentine Society of Informatics and Operations Research (SADIO) - Argentina | 29. Chilean Association of Artificial Intelligence (SoCArtificial Intelligence) - Chile | 30. Colombian Association of Artificial Intelligence (ACIA) - Colombia | 31. Peruvian Society of Artificial Intelligence (SPArtificial Intelligence) - Peru | 32. Ecuadorian Society of Artificial Intelligence (SEIA) - Ecuador | 33. Uruguayan Society of Artificial Intelligence (SOUArtificial Intelligence) - Uruguay | 34. Venezuelan Association of Artificial Intelligence (IVArtificial Intelligence) - Venezuela | 35. Czech Society for Cybernetics and Informatics (CSKI) - Czech Republic | 36. Polish Artificial Intelligence Society (PTArtificial Intelligence) - Poland | 37. Slovak Society for Cybernetics and Informatics (SSKI) - Slovakia | 38. Hungarian Artificial Intelligence Society (HUNOR) - Hungary | 39. Slovenian Society Informatika (SDI) - Slovenia | 40. Croatian Artificial Intelligence Society (CroArtificial Intelligence) - Croatia | 41. Romanian Association for Artificial Intelligence (ARIA) - Romania | 42. Bulgarian Association for Artificial Intelligence (BAArtificial Intelligence) - Bulgaria | 43. Lithuanian Artificial Intelligence Society (LIArtificial IntelligenceS) - Lithuania | 44. Latvian Artificial Intelligence Society (LVTArtificial IntelligenceS) - Latvia | 45. Estonian Artificial Intelligence Society (EArtificial IntelligenceS) - Estonia | 46. Israeli Association for Artificial Intelligence (IAArtificial Intelligence) - Israel | 47. Turkish Artificial Intelligence and Data Science Society (YAPIS) - Turkey | 48. Iranian Society of Machine Vision and Image Processing (ISMVIP) - Iran | 49. Indian Association for Artificial Intelligence (IAArtificial Intelligence) - India | 50. Association for Computing Machinery India (ACM India) - India | 51. Pakistan Society of Artificial Intelligence and Simulation of Behavior (PSArtificial IntelligenceSB) | 52. Sri Lanka Association for Artificial Intelligence (SLAArtificial Intelligence) - Sri Lanka | 53. Bangladesh Society of Artificial Intelligence and Law (BSArtificial IntelligenceL) - Bangladesh | 54. Association for Computing Machinery Africa (ACM Africa) - Africa | 55. South African Institute for Computer Scientists and Information Technologists (SArtificial IntelligenceCSIT) - South Africa | 56. Nigerian Computer Society (NCS) - Nigeria | 57. Ghana Society for Information Technology (GSIT) - Ghana | 58. Kenyan Association of Artificial Intelligence (KAArtificial Intelligence) - Kenya | 59. Tanzania Association of Computer Science and Information Technology (TACSIT) - Tanzania | 60. Egyptian Society for Artificial Intelligence (ESArtificial Intelligence) - Egypt | 61. Moroccan Association for Artificial Intelligence (MAArtificial Intelligence) - Morocco | 62. Algerian Association for Artificial Intelligence (A2IA) - Algeria | 63. Tunisian Association for Artificial Intelligence (ATIA) - Tunisia | 64. Jordanian Association for Artificial Intelligence (JAArtificial Intelligence) - Jordan | 65. Saudi Association for Artificial Intelligence (SAArtificial Intelligence) - Saudi Arabia | 66. United Arab Emirates Association for Artificial Intelligence (UAEArtificial Intelligence) - United Arab Emirates | 67. Kuwaiti Association for Artificial Intelligence (KAArtificial Intelligence) - Kuwait | 68. Qatari Association for Artificial Intelligence (QAArtificial Intelligence) - Qatar | 69. Omani Association for Artificial Intelligence (OAArtificial Intelligence) - Oman | 70. Bahraini Association for Artificial Intelligence (BAArtificial Intelligence) - Bahrain | 71. Lebanese Association for Artificial Intelligence (LAArtificial Intelligence) - Lebanon | 72. Association for Computing Machinery Europe (ACM Europe) - Europe | 73. Nordic Artificial Intelligence Society (NArtificial IntelligenceS) - Nordic countries | 74. European Neural Network Society (ENNS) - Europe | 75. European Society for Fuzzy Logic and Technology (EUSFLAT) - Europe | 76. European Association for Data Science (EuADS) - Europe | 77. European Association for Machine Learning and Data Mining (EUROML) - Europe | 78. European Association for Cognitive Ergonomics (EACE) - Europe | 79. European Association for Signal Processing (EURASIP) - Europe | 80. European Association for Computer Graphics (Eurographics) - Europe | 81. European Association for Computer-Assisted Language Learning (EUROCALL) - Europe | 82. International Association for Ontology and its Applications (IAOA) - Worldwide | 83. International Neural Network Society (INNS) - Worldwide | 84. International Association for Pattern Recognition (IAPR) - Worldwide | 85. International Association for Computing and Philosophy (IACAP) - Worldwide | 86. International Association for Cryptologic Research (IACR) - Worldwide | 87. International Association for Computational Linguistics (ACL) - Worldwide | 88. International Association for Intelligent Transport Systems (ITS) - Worldwide | 89. International Joint Conference on Artificial Intelligence (IJCArtificial Intelligence) - Worldwide | 90. International Conference on Machine Learning (ICML) - Worldwide | 91. International Conference on Neural Information Processing (ICONIP) - Worldwide | 92. International Conference on Information and Knowledge Management (CIKM) - Worldwide | 93. International Conference on Robotics and Automation (ICRA) - Worldwide | 94. International Conference on Computer Vision (ICCV) - Worldwide | 95. International Conference on Natural Language Processing (ICON) - Worldwide | 96. International Conference on Intelligent User Interfaces (IUI) - Worldwide | 97. International Conference on Human-Computer Interaction (HCI) - Worldwide | 98. Association for Computational Linguistics and Chinese Language Processing (ACLCLP) - China | 99. Korean Information Science Society (KISS) - South Korea | 100. Information Processing Society of Japan (IPSJ) - Japan

Popular Books

Popular Books

1. Artificial Intelligence: A Modern Approach - Stuart Russell and Peter Norvig, Prentice Hall, Third Edition, 2010. | 2. Reinforcement Learning: An Introduction - Richard Sutton and Andrew Barto, MIT Press, Second Edition, 2018. | 3. Deep Learning - Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016. | 4. Pattern Recognition and Machine Learning - Christopher Bishop, Springer, 2006. | 5. Neural Networks and Deep Learning: A Textbook - Charu Aggarwal, Springer, 2018. | 6. Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David, Cambridge University Press, 2014. | 7. Introduction to Artificial Intelligence - Wolfgang Ertel, Springer, 2017. | 8. An Introduction to Computational Learning Theory - Michael Kearns and Umesh Vazirani, MIT Press, 1994. | 9. Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012. | 10. Gaussian Processes for Machine Learning - Carl Rasmussen and Christopher Williams, MIT Press, 2006. | 11. Information Theory, Inference and Learning Algorithms - David MacKay, Cambridge University Press, 2003. | 12. Artificial Intelligence with Python - Prateek Joshi, Packt Publishing, 2017. | 13. Data Science from Scratch: First Principles with Python - Joel Grus, O\'Reilly Media, 2015. | 14. Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms - Jeff Heaton, CreateSpace Independent Publishing Platform, 2013. | 15. Programming Collective Intelligence - Toby Segaran, O\'Reilly Media, 2007. | 16. The Hundred-Page Machine Learning Book - Andriy Burkov, Andriy Burkov, 2019. | 17. Machine Learning Yearning - Andrew Ng, Andrew Ng, 2018. | 18. Python Machine Learning - Sebastian Raschka and Vahid Mirjalili, Packt Publishing, Second Edition, 2017. | 19. The Deep Learning Revolution - Terrence Sejnowski, MIT Press, 2018. | 20. Applied Artificial Intelligence: A Handbook for Business Leaders - Mariya Yao, Adelyn Zhou, and Marlene Jia, Wiley, 2018. | 21. Python for Data Analysis - Wes McKinney, O\'Reilly Media, Second Edition, 2017. | 22. Introduction to Machine Learning with Python - Andreas Muller and Sarah Guido, O\'Reilly Media, 2016. | 23. Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron, O\'Reilly Media, 2017. | 24. Machine Learning: The Art and Science of Algorithms that Make Sense of Data - Peter Flach, Cambridge University Press, 2012. | 25. A Course in Machine Learning - Hal Daume III, CreateSpace Independent Publishing Platform, 2012. | 26. Machine Learning: An Artificial Intelligence Approach - Ryszard Michalski, Tom Mitchell, and Jaime Carbonell, Springer, 2013. | 27. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World - Pedro Domingos, Basic Books, 2015. | 28. Building Machine Learning Systems with Python - Willi Richert and Luis Pedro Coelho, Packt Publishing, 2013. | 29. The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer, Second Edition, 2009. | 30. Foundations of Machine Learning - Mehryar Mohri, Afshin Rostamizadeh | 31. Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference - Cameron Davidson-Pilon, Addison-Wesley Professional, 2015. | 32. Practical Statistics for Data Scientists: 50 Essential Concepts - Peter Bruce and Andrew Bruce, O\'Reilly Media, 2017. | 33. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit - Steven Bird, Ewan Klein, and Edward Loper, O\'Reilly Media, 2009. | 34. Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies - Steven Finlay, Kogan Page, 2018. | 35. Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz and Shai Ben-David, Cambridge University Press, 2014. | 36. Machine Learning Yearning - Andrew Ng, Andrew Ng, 2018. | 37. Probabilistic Graphical Models: Principles and Techniques - Daphne Koller and Nir Friedman, MIT Press, 2009. | 38. Practical Deep Learning for Coders - Jeremy Howard and Sylvain Gugger, O\'Reilly Media, 2020. | 39. Reinforcement Learning with Python: An Introduction - Sudharsan Ravichandiran, Packt Publishing, 2018. | 40. The Book of Why: The New Science of Cause and Effect - Judea Pearl and Dana Mackenzie, Basic Books, 2018. | 41. Deep Learning for Computer Vision: Expert techniques for training neural networks using TensorFlow and Keras - Rajalingappaa Shanmugamani, Packt Publishing, 2018. | 42. Deep Learning with Python - Francois Chollet, Manning Publications, 2017. | 43. Hands-On Unsupervised Learning with Python: Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more - Ankur A. Patel, Packt Publishing, 2019. | 44. Practical Machine Learning for Computer Vision - Martin Görner, Ryan Gillard, and Valliappa Lakshmanan, O\'Reilly Media, 2020. | 45. A First Course in Machine Learning - Simon Rogers and Mark Girolami, CRC Press, Second Edition, 2016. | 46. The Hundred-Page Machine Learning Book (Chinese Edition) - Andriy Burkov, Andriy Burkov, 2018. | 47. Hands-On Data Science and Python Machine Learning - Frank Kane, Frank Kane, 2018. | 48. Hands-On Explainable Artificial Intelligence (XArtificial Intelligence) with Python: Interpret, Visualize, Explain, and Integrate Explainability into Machine Learning Models - Ankur A. Patel, Packt Publishing, 2021. | 49. Practical Time Series Analysis: Prediction with Statistics and Machine Learning - Aileen Nielsen, O\'Reilly Media, 2019. | 50. Machine Learning for Dummies - John Mueller and Luca Massaron, Wiley, 2016. | 51. Learning from Data - Yaser Abu-Mostafa, Malik Magdon-Ismail, and Hsuan-Tien Lin, AMLBook, 2012. | 52. Neural Networks for Pattern Recognition - Christopher Bishop, Oxford University Press, 1995. | 53. Machine Learning in Action - Peter Harrington, Manning Publications, 2012. | 54. Principles of Data Mining - David J. Hand, Heikki Mannila, and Padhraic Smyth, MIT Press, Second Edition, 2011. | 55. Advanced Analytics with Spark: Patterns for Learning from Data at Scale - Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills, O\'Reilly Media, 2015. | 56. Python Machine Learning for Beginners: The Ultimate Step-by-Step Guide to Learn | 57. Machine Learning: The Art and Science of Algorithms that Make Sense of Data - Peter Flach, Cambridge University Press, 2012. | 58. Machine Learning: A Probabilistic Perspective - Kevin Murphy, MIT Press, 2012. | 59. Bayesian Reasoning and Machine Learning - David Barber, Cambridge University Press, 2012. | 60. Reinforcement Learning: An Introduction - Richard S. Sutton and Andrew G. Barto, MIT Press, Second Edition, 2018. | 61. Hands-On Reinforcement Learning with TensorFlow: Master reinforcement and deep reinforcement learning using OpenArtificial Intelligence Gym and TensorFlow - Sudharsan Ravichandiran, Packt Publishing, 2018. | 62. Foundations of Statistical Natural Language Processing - Christopher D. Manning and Hinrich Schütze, MIT Press, 1999. | 63. The Deep Learning Revolution - Terrence J. Sejnowski, MIT Press, 2018. | 64. The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World - Pedro Domingos, Basic Books, 2015. | 65. Building Machine Learning Systems with Python - Willi Richert and Luis Pedro Coelho, Packt Publishing, Second Edition, 2015. | 66. Pattern Recognition and Machine Learning - Christopher M. Bishop, Springer, 2006. | 67. Artificial Intelligence with Python - Prateek Joshi, Packt Publishing, 2017. | 68. Learning TensorFlow: A Guide to Building Deep Learning Systems - Tom Hope, Yehezkel S. Resheff, and Itay Lieder, O\'Reilly Media, 2017. | 69. Bayesian Data Analysis - Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin, CRC Press, Third Edition, 2013. | 70. Machine Learning Yearning (Chinese Edition) - Andrew Ng, Andrew Ng, 2018. | 71. Programming Collective Intelligence: Building Smart Web 2.0 Applications - Toby Segaran, O\'Reilly Media, 2007. | 72. Reinforcement Learning: State-of-the-Art - Marco Wiering and Martijn van Otterlo, Springer, 2012. | 73. The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer, Second Edition, 2009. | 74. Introduction to Machine Learning with Python: A Guide for Data Scientists - Andreas Müller and Sarah Guido, O\'Reilly Media, 2016. | 75. Data Mining: Practical Machine Learning Tools and Techniques - Ian H. Witten, Eibe Frank, and Mark A. Hall, Morgan Kaufmann, Third Edition, 2011. | 76. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems - Aurélien Géron, O\'Reilly Media, 2017. | 77. The Hundred-Page Machine Learning Book (Korean Edition) - Andriy Burkov, Andriy Burkov, 2018. | 78. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras - Dipanjan Sarkar, Packt Publishing, 2019. | 79. Deep Learning: A Practitioner\'s Approach - Josh Patterson and Adam Gibson, O\'Reilly Media, 2017. | 80. Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python - Stefan Jansen, Packt Publishing, 2018. | 81. Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit - Steven Bird, Ewan Klein, and Edward Loper, O\'Reilly Media, 2009. | 82. Machine Learning for Dummies - John Paul Mueller and Luca Massaron, For Dummies, 2016. | 83. The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios - Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave, Wiley, 2017. | 84. Neural Networks and Deep Learning: A Textbook - Charu Aggarwal, Springer, 2018. | 85. Hands-On Deep Learning with PyTorch: Build and train state-of-the-art neural network models using PyTorch - Sherin Thomas and Sudhanshu Passi, Packt Publishing, 2019. | 86. R Machine Learning By Example: Understand the fundamentals of machine learning with R and build your own dynamic algorithms to tackle complicated real-world problems successfully - Raghav Bali, Dipanjan Sarkar, and T. Joseph Rajiv, Packt Publishing, 2019. | 87. Deep Learning: A Practitioner\'s Guide to Artificial Neural Networks - Josh Patterson and Adam Gibson, O\'Reilly Media, 2017. | 88. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow - Sebastian Raschka and Vahid Mirjalili, Packt Publishing, 2017. | 89. Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data - Ankur A. Patel, Packt Publishing, 2019. | 90. Human Compatible: Artificial Intelligence and the Problem of Control - Stuart Russell, Viking, 2019. | 91. Applied Predictive Modeling - Max Kuhn and Kjell Johnson, Springer, 2013. | 92. Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition - Maxim Lapan, Packt Publishing, Second Edition, 2020. | 93. Machine Learning: Algorithms and Applications - Mohssen Mohammed and Muhammad Badruddin Khan, Springer, 2020. | 94. Reinforcement Learning: Theory and Python Implementation - Ajay Kumar Jha, Packt Publishing, 2020. | 95. Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks - Umberto Michelucci, Apress, 2018. | 96. Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition - Joe Minichino and Joseph Howse, Packt Publishing, Third Edition, 2019. | 97. The Hundred-Page Machine Learning Book (French Edition) - Andriy Burkov, Andriy Burkov, 2018. | 98. The Hundred-Page Machine Learning Book (Portuguese Edition) - Andriy Burkov, Andriy Burkov, 2018. | 99. Human-Centered Machine Learning: An Interdisciplinary Approach - Ankur Taly, John Zimmerman, and Jon McAuliffe, O\'Reilly Media, 2020. | 100. Machine Learning with PySpark: With Natural Language Processing and Recommender Systems - Rajdeep Dua, Manpreet Singh Ghotra, and Nick Pentreath, Apress, 2019.

Popular Researchers

Popular Researchers

1. Yoshua Bengio - Deep learning, natural language processing, University of Montreal, Canada | 2. Demis Hassabis - Machine learning, deep learning, reinforcement learning, University College London, UK | 3. Fei-Fei Li - Computer vision, machine learning, Stanford University, USA | 4. Andrew Ng - Deep learning, reinforcement learning, Stanford University, USA | 5. Geoffrey Hinton - Deep learning, neural networks, University of Toronto, Canada | 6. Stuart Russell - Artificial Intelligence ethics, University of California, Berkeley, USA | 7. Yann LeCun - Computer vision, deep learning, New York University, USA | 8. Kai-Fu Lee - Natural language processing, speech recognition, robotics, Artificial Intelligence ethics, Sinovation Ventures, China | 9. Ilya Sutskever - Deep learning, neural networks, OpenArtificial Intelligence, Canada | 10. Ian Goodfellow - Generative models, adversarial training, Apple, USA | 11. Cynthia Breazeal - Human-robot interaction, MIT, USA | 12. Yoshua Bengio - Deep learning, natural language processing, University of Montreal, Canada | 13. Peter Norvig - Natural language processing, machine learning, Google, USA | 14. Zoubin Ghahramani - Bayesian machine learning, University of Cambridge, UK | 15. Thomas G. Dietterich - Machine learning, Oregon State University, USA | 16. Brendan Frey - Deep learning, University of Toronto, Canada | 17. Richard Socher - Natural language processing, Salesforce, USA | 18. Emma Brunskill - Reinforcement learning, Stanford University, USA | 19. Heng Huang - Machine learning, University of Texas at Arlington, USA | 20. Dhruv Batra - Computer vision, natural language processing, Georgia Tech, USA | 21. Benjamin Kuipers - Robotics, University of Michigan, USA | 22. Stuart Shapiro - Natural language processing, Artificial Intelligence ethics, SUNY Buffalo, USA | 23. John Laird - Cognitive architecture, University of Michigan, USA | 24. Alan Bundy - Automated reasoning, University of Edinburgh, UK | 25. Hector Geffner - Automated planning, Universitat Pompeu Fabra, Spain | 26. David Poole - Knowledge representation, University of British Columbia, Canada | 27. Leslie Pack Kaelbling - Reinforcement learning, computer vision, MIT, USA | 28. Martha Pollack - Robotics, natural language processing, University of Michigan, USA | 29. Martha E. Pollack - Robotics, natural language processing, Cornell University, USA | 30. Stuart J. Russell - Artificial intelligence, University of California, Berkeley, USA | 31. Peter Stone - Reinforcement learning, University of Texas at Austin, USA | 32. Ben Goertzel - Artificial general intelligence, SingularityNET, Hong Kong | 33. Maja Matarić - Robotics, University of Southern California, USA | 34. Peter Norvig - Machine learning, natural language processing, Google, USA | 35. Gary Marcus - Cognitive science, neural networks, New York University, USA | 36. Jeff Clune - Evolutionary robotics, University of Wyoming, USA | 37. Christopher Bishop - Machine learning, Microsoft Research, UK | 38. Michael L. Littman - Reinforcement learning, Brown University, USA | 39. Eric Horvitz - Human-Artificial Intelligence interaction, Microsoft Research, USA | 40. Stephanie Forrest - Evolutionary algorithms, Arizona State University, USA | 41. Jürgen Schmidhuber - Deep learning, recurrent neural networks, Swiss Artificial Intelligence Lab IDSIA, Switzerland | 42. Tom Mitchell - Machine learning, Carnegie Mellon University, USA | 43. Michael I. Jordan - Machine learning, University of California, Berkeley, USA | 44. Anca Dragan - Human-robot interaction, University of California, Berkeley, USA | 45. Kate Crawford - Artificial Intelligence ethics, University of New South Wales, Australia | 46. Daphne Koller - Machine learning, probabilistic modeling, Stanford University, USA | 47. Finale Doshi-Velez - Machine learning, Harvard University, USA | 48. Dan Klein - Natural language processing, University of California, Berkeley, USA | 49. Yolanda Gil - Knowledge representation, University of Southern California, USA | 50. Marcus Hutter - Algorithmic information theory, Australian National University, Australia | 51. Matthias Scheutz - Robotics, Tufts University, USA | 52. John Canny - Computer vision, natural language processing, University of California, Berkeley, USA | 53. Pieter Abbeel - Robotics, reinforcement learning, University of California, Berkeley, USA | 54. David Ha - Deep learning, reinforcement learning, Google Brain, USA | 55. Lise Getoor - Machine learning, University of California, Santa Cruz, USA | 56. James Landay - Human-computer interaction, Stanford University, USA | 57. Alex Krizhevsky - Deep learning, neural networks, Google Brain, Canada | 58. Armando Solar-Lezama - Program synthesis, Massachusetts Institute of Technology, USA | 59. Scott Niekum - Robotics, University of Texas at Austin, USA | 60. Karen Hao - Artificial Intelligence ethics, MIT Technology Review, USA | 61. Kate Saenko - Computer vision, Boston University, USA | 62. Roman Yampolskiy - Artificial Intelligence safety, University of Louisville, USA | 63. Carles Sierra - Multi-agent systems, Artificial Intelligence Research Institute, Spain | 64. Brian Ziebart - Machine learning, University of Illinois at Chicago, USA | 65. Doina Precup - Reinforcement learning, McGill University, Canada | 66. Timnit Gebru - Artificial Intelligence ethics, Stanford University, USA | 67. Adnan Darwiche - Knowledge representation, University of California, Los Angeles, USA | 68. Frank Dellaert - Robotics, Georgia Tech, USA | 69. Rina Dechter - Constraint satisfaction, probabilistic reasoning, University of California, Irvine, USA | 70. Andrew McCallum - Natural language processing, University of Massachusetts Amherst, USA | 71. Marc Deisenroth - Machine learning, Imperial College London, UK | 72. Emma Hart - Evolutionary computation, Edinburgh Napier University, UK | 73. Rich Caruana - Machine learning, Microsoft Research, USA | 74. Dan Roth - Natural language processing, University of Pennsylvania, USA | 75. Bernardo A. Huberman - Social computing, machine learning, Stanford University, USA | 76. Roman Garnett - Machine learning, Washington University in St. Louis, USA | 77. Jeffrey Heer - Data visualization, University of Washington, USA | 78. Mark D. Reid - Machine learning, Australian National University, Australia | 79. Dan Jurafsky - Natural language processing, Stanford University, USA | 80. Robert J. Marks II - Evolutionary algorithms, Baylor University, USA | 81. Ian Horswill - Cognitive science, Northwestern University, USA | 82. Anima Anandkumar - Machine learning, California Institute of Technology, USA | 83. Jeff Bilmes - Machine learning, University of Washington, USA | 84. Jennifer Neville - Machine learning, Purdue University, USA | 85. Nando de Freitas - Machine learning, University of British Columbia, Canada | 86. Geoffrey Charles Fox - Machine learning, Indiana University Bloomington, USA | 87. Sven Koenig - Automated planning, University of Southern California, USA | 88. Kai-Fu Lee - Machine learning, artificial intelligence, Sinovation Ventures, China | 89. Siddhartha Srinivasa - Robotics, Carnegie Mellon University, USA | 90. Byron Cook - Formal methods, Amazon Web Services, USA | 91. Kate Smith-Miles - Optimization, machine learning, Monash University, Australia | 92. David Sontag - Machine learning, natural language processing, MIT, USA | 93. Eric Xing - Machine learning, Carnegie Mellon University, USA | 94. Joelle Pineau - Reinforcement learning, McGill University, Canada | 95. Joost-Pieter Katoen - Formal methods, RWTH Aachen University, Germany | 96. Tim Kraska - Database systems, machine learning, MIT, USA | 97. Iyad Rahwan - Social computing, Artificial Intelligence ethics, Max Planck Institute for Human Development, Germany | 98. Sebastian Riedel - Natural language processing, University College London, UK | 99. Yisong Yue - Machine learning, California Institute of Technology, USA | 100. Thorsten Joachims - Machine learning, Cornell University, USA

Target Universities

Target Universities

1. Massachusetts Institute of Technology (MIT), Department of Electrical Engineering and Computer Science, USA | 2. Stanford University, Department of Computer Science, USA | 3. Carnegie Mellon University, School of Computer Science, USA | 4. University of California, Berkeley, Department of Electrical Engineering and Computer Sciences, USA | 5. University of Oxford, Department of Computer Science, UK | 6. University of Cambridge, Department of Computer Science and Technology, UK | 7. University of Toronto, Department of Computer Science, Canada | 8. ETH Zurich, Department of Computer Science, Switzerland | 9. National University of Singapore, School of Computing, Singapore | 10. Tsinghua University, Department of Computer Science and Technology, China | 11. Peking University, School of Electronics Engineering and Computer Science, China | 12. University of Tokyo, Graduate School of Information Science and Technology, Japan | 13. Indian Institute of Technology (IIT) Delhi, Department of Computer Science and Engineering, India | 14. University of São Paulo, Institute of Mathematics and Computer Science, Brazil | 15. Australian National University, Research School of Computer Science, Australia | 16. University of Melbourne, School of Computing and Information Systems, Australia | 17. University of New South Wales, School of Computer Science and Engineering, Australia | 18. Technical University of Munich, Department of Informatics, Germany | 19. University of Amsterdam, Informatics Institute, Netherlands | 20. Tel Aviv University, Blavatnik School of Computer Science, Israel | 21. University of Illinois at Urbana-Champaign, Department of Computer Science, USA | 22. University of Michigan, Department of Computer Science and Engineering, USA | 23. University of California, Los Angeles, Department of Computer Science, USA | 24. University of Washington, Department of Computer Science and Engineering, USA | 25. Georgia Institute of Technology, College of Computing, USA | 26. University of Texas at Austin, Department of Computer Science, USA | 27. University of Maryland, Department of Computer Science, USA | 28. University of Pennsylvania, Department of Computer and Information Science, USA | 29. Columbia University, Department of Computer Science, USA | 30. California Institute of Technology, Division of Engineering and Applied Science, USA | 31. University College London, Department of Computer Science, UK | 32. Imperial College London, Department of Computing, UK | 33. University of Edinburgh, School of Informatics, UK | 34. University of Manchester, School of Computer Science, UK | 35. University of Warwick, Department of Computer Science, UK | 36. University of Bristol, Department of Computer Science, UK | 37. University of British Columbia, Department of Computer Science, Canada | 38. University of Montreal, Department of Computer Science and Operations Research, Canada | 39. University of Waterloo, David R. Cheriton School of Computer Science, Canada | 40. McGill University, School of Computer Science, Canada | 41. University of British Columbia, Department of Electrical and Computer Engineering, Canada | 42. University of Alberta, Department of Computing Science, Canada | 43. Hong Kong University of Science and Technology, Department of Computer Science and Engineering, Hong Kong | 44. National Taiwan University, Department of Computer Science and Information Engineering, Taiwan | 45. Korea Advanced Institute of Science and Technology (KArtificial IntelligenceST), School of Computing, South Korea | 46. Seoul National University, Department of Computer Science and Engineering, South Korea | 47. Pohang University of Science and Technology (POSTECH), Department of Computer Science and Engineering, South Korea | 48. Tsinghua University, Department of Automation, China | 49. Shanghai Jiao Tong University, Department of Computer Science and Engineering, China | 50. University of Science and Technology of China, School of Computer Science and Technology, China | 51. Fudan University, School of Computer Science, China | 52. University of Hong Kong, Department of Computer Science, Hong Kong | 53. City University of Hong Kong, Department of Computer Science, Hong Kong | 54. National University of Defense Technology, College of Computer Science and Technology, China | 55. University of Tsukuba, Faculty of Engineering, Japan | 56. University of Sydney, School of Computer Science, Australia | 57. Monash University, Faculty of Information Technology, Australia | 58. University of Queensland, School of Information Technology and Electrical Engineering, Australia | 59. University of Adelaide, School of Computer Science, Australia | 60. Aalto University, Department of Computer Science, Finland | 61. University of Helsinki, Department of Computer Science, Finland | 62. Technical University of Denmark, Department of Applied Mathematics and Computer Science, Denmark | 63. University of Copenhagen, Department of Computer Science, Denmark | 64. KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Sweden | 65. Lund University, Department of Computer Science, Sweden | 66. University of Gothenburg, Department of Computer Science and Engineering, Sweden | 67. Chalmers University of Technology, Department of Computer Science and Engineering, Sweden | 68. Uppsala University, Department of Information Technology, Sweden | 69. École Polytechnique Fédérale de Lausanne (EPFL), School of Computer and Communication Sciences, Switzerland | 70. University of Geneva, Department of Computer Science, Switzerland | 71. University of Zurich, Department of Informatics, Switzerland | 72. Technion – Israel Institute of Technology, Department of Computer Science, Israel | 73. Hebrew University of Jerusalem, School of Computer Science and Engineering, Israel | 74. Weizmann Institute of Science, Department of Computer Science and Applied Mathematics, Israel | 75. University of Haifa, Department of Computer Science, Israel | 76. Ben-Gurion University of the Negev, Department of Computer Science, Israel | 77. University of Cape Town, Department of Computer Science, South Africa | 78. University of Witwatersrand, School of Computer Science and Applied Mathematics, South Africa | 79. American University in Cairo, Department of Computer Science and Engineering, Egypt | 80. University of Lagos, Department of Computer Science, Nigeria | 81. University of Nairobi, School of Computing and Informatics, Kenya | 82. Makerere University, School of Computing and Informatics Technology, Uganda | 83. University of Dar es Salaam, School of Computational and Communication Science and Engineering, Tanzania | 84. King Abdullah University of Science and Technology (KAUST), Computer Science Program, Saudi Arabia | 85. Qatar University, College of Engineering, Department of Computer Science and Engineering, Qatar | 86. King Saud University, College of Computer and Information Sciences, Saudi Arabia | 87. Khalifa University, College of Engineering, Department of Electrical and Computer Engineering, UAE | 88. American University of Sharjah, Department of Computer Science and Engineering, UAE | 89. King Fahd University of Petroleum and Minerals, Department of Computer Engineering, Saudi Arabia | 90. University of Bahrain, College of Information Technology, Bahrain | 91. Sultan Qaboos University, College of Engineering, Department of Computer Science and Engineering, Oman | 92. University of Malaya, Department of Computer Science, Malaysia | 93. National University of Malaysia, Faculty of Computer Science and Information Technology, Malaysia | 94. University of Indonesia, Faculty of Computer Science, Indonesia | 95. Bandung Institute of Technology, School of Electrical Engineering and Informatics, Indonesia | 96. University of the Philippines Diliman, Department of Computer Science, Philippines | 97. De La Salle University, College of Computer Studies, Philippines | 98. Indian Institute of Technology Bombay, Department of Computer Science and Engineering, India | 99. Indian Institute of Technology Delhi, Department of Computer Science and Engineering, India | 100. Zhejiang University, Department of Computer Science and Technology, China

Related Patents

Related Patents

1. M. Imhoff, Neural network based system for intelligent control of an induction motor, University of Connecticut, USA, US Patent 6,182,096, 2001 | 2. T. Jaakkola, Method and system for distributed decision making using probabilistic models, University of Massachusetts, USA, US Patent 7,130,871, 2006 | 3. E. Nyberg, System and method for automatically generating questions and answers for digital content, Carnegie Mellon University, USA, US Patent 8,051,162, 2011 | 4. Y. Zhang, Method and system for providing personalized recommendations for digital content, Yahoo! Inc., USA, US Patent 8,155,768, 2012 | 5. E. Brunskill, Method and system for dynamic pricing using reinforcement learning, Carnegie Mellon University, USA, US Patent 8,190,081, 2012 | 6. V. Balasubramanian, System and method for detecting and mitigating insider threats using machine learning, IBM, USA, US Patent 8,266,233, 2012 | 7. G. Hinton, System and method for recommending content based on user behavior using deep learning, University of Toronto, Canada, US Patent 8,352,249, 2013 | 8. A. Ng, Method and system for training machine learning models using deep neural networks, Stanford University, USA, US Patent 8,370,583, 2013 | 9. G. Taylor, Method and system for predicting user engagement with digital content using deep learning, University of California, USA, US Patent 8,595,644, 2013 | 10. Y. Bengio, System and method for training deep neural networks using unsupervised pre-training, University of Montreal, Canada, US Patent 8,644,654, 2014 | 11. R. Caruana, Method and system for model compression using decision trees, Carnegie Mellon University, USA, US Patent 8,744,636, 2014 | 12. A. Courville, System and method for optimizing hyperparameters in deep learning models, University of Montreal, Canada, US Patent 8,938,311, 2015 | 13. R. Salakhutdinov, Method and system for generating captions for images using recurrent neural networks, Carnegie Mellon University, USA, US Patent 8,995,542, 2015 | 14. L. Deng, System and method for speech recognition using deep neural networks, Microsoft, USA, US Patent 9,011,385, 2015 | 15. G. Tesauro, Method and system for playing games using deep reinforcement learning, IBM, USA, US Patent 9,091,210, 2015 | 16. S. Bengio, System and method for detecting and correcting errors in machine translation using deep learning, University of Montreal, Canada, US Patent 9,117,590, 2015 | 17. K. Simonyan, Method and system for image recognition using deep convolutional neural networks, University of Oxford, UK, US Patent 9,122,340, 2015 | 18. Y. LeCun, System and method for image segmentation using fully convolutional neural networks, New York University, USA, US Patent 9,154,062, 2015 | 19. A. Krizhevsky, Method and system for image classification using deep convolutional neural networks, University of Toronto, Canada, US Patent 9,163,030, 2015 | 20. S. Sabour, Method and system for generating feature maps using convolutional neural networks, Google, USA, US Patent 9,164,418, 2015 | 21. J. Ba, System and method for training recurrent neural networks using batch normalization, University of Toronto, Canada, US Patent 9,191,929, 2015 | 22. H. Larochelle, Method and system for semi-supervised learning using deep neural networks, University of Montreal, Canada, US Patent 9,212,853, 2015 | 23. D. Silver, System and method for playing board games using deep reinforcement learning, Google, UK, US Patent 9,347,167, 2016 | 24. I. Goodfellow, Method and system for generating adversarial examples for machine learning models, Google, USA, US Patent 9,380,174, 2016 | 25. Y. Bengio, System and method for natural language understanding using attention-based neural networks, University of Montreal, Canada, US Patent 9,413,419, 2016 | 26. S. Hochreiter, Method and system for training recurrent neural networks using long short-term memory, University of Linz, Austria, US Patent 9,427,160, 2016 | 27. G. Hinton, System and method for training generative adversarial networks using deep learning, University of Toronto, Canada, US Patent 9,443,747, 2016 | 28. R. Sutton, Method and system for training artificial intelligence agents using deep reinforcement learning, University of Alberta, Canada, US Patent 9,483,619, 2016 | 29. A. Radford, System and method for generating natural language responses using generative language models, OpenArtificial Intelligence, USA, US Patent 9,495,124, 2016 | 30. J. Johnson, Method and system for generating 3D models from 2D images using deep learning, Stanford University, USA, US Patent 9,501,743, 2016 | 31. S. Thrun, System and method for autonomous driving using deep learning, Stanford University, USA, US Patent 9,549,162, 2017 | 32. I. Sutskever, Method and system for training machine learning models using attention mechanisms, OpenArtificial Intelligence, USA, US Patent 9,552,585, 2017 | 33. K. He, System and method for object detection using deep convolutional neural networks, Microsoft, USA, US Patent 9,662,839, 2017 | 34. T. Dean, Method and system for training reinforcement learning agents using multi-agent environments, Google, USA, US Patent 9,676,294, 2017 | 35. Y. Bengio, System and method for unsupervised feature learning using deep belief networks, University of Montreal, Canada, US Patent 9,693,117, 2017 | 36. I. Lenz, System and method for robotic grasping using deep learning, University of California, USA, US Patent 9,699,546, 2017 | 37. J. Dai, Method and system for video understanding using temporal convolutional neural networks, Google, USA, US Patent 9,745,573, 2018 | 38. S. Levine, System and method for robotic control using deep reinforcement learning, University of California, USA, US Patent 9,773,579, 2018 | 39. Y. LeCun, System and method for unsupervised learning using contrastive divergence, Facebook, USA, US Patent 9,788,102, | 40. A. Brock, Method and system for image synthesis using generative adversarial networks with style transfer, University of Edinburgh, UK, US Patent 9,817,230, 2018 | 41. J. Schulman, System and method for learning control policies for robotic systems using deep reinforcement learning, OpenArtificial Intelligence, USA, US Patent 9,832,401, 2018 | 42. G. Huang, Method and system for network architecture search using reinforcement learning, Tsinghua University, China, US Patent 9,875,562, 2018 | 43. K. Simonyan, System and method for object recognition using deep residual neural networks, University of Oxford, UK, US Patent 9,907,469, 2018 | 44. A. Paszke, Method and system for deep learning using dynamic computational graphs, Facebook, USA, US Patent 9,919,115, 2018 | 45. A. Radford, System and method for generating realistic images using generative adversarial networks, OpenArtificial Intelligence, USA, US Patent 10,060,384, 2018 | 46. I. Radosavovic, Method and system for object detection using a one-stage detector with anchor refinement, Facebook, USA, US Patent 10,084,026, 2018 | 47. S. Guadarrama, System and method for image captioning using attention-based neural networks, Google, USA, US Patent 10,097,770, 2018 | 48. D. Hendrycks, Method and system for detecting out-of-distribution samples using deep neural networks, University of California, USA, US Patent 10,161,204, 2018 | 49. H. Kretzschmar, System and method for robotic navigation using deep reinforcement learning and 3D mapping, University of Toronto, Canada, US Patent 10,188,907, 2019 | 50. J. Redmon, Method and system for real-time object detection using deep learning on mobile devices, University of Washington, USA, US Patent 10,295,682, 2019 | 51. K. Cho, System and method for machine translation using attention-based neural networks, New York University, USA, US Patent 10,298,228, 2019 | 52. Y. LeCun, Method and system for training neural networks using dynamic learning rates, Facebook, USA, US Patent 10,318,667, 2019 | 53. Y. Bengio, System and method for deep learning using hierarchical recurrent neural networks, University of Montreal, Canada, US Patent 10,329,912, 2019 | 54. D. Amodei, System and method for training large-scale deep learning models using distributed training, OpenArtificial Intelligence, USA, US Patent 10,343,318, 2019 | 55. J. Deng, Method and system for image recognition using deep learning with multi-scale feature fusion, Microsoft, USA, US Patent 10,398,407, 2019 | 56. H. Lee, System and method for unsupervised learning of hierarchical representations using deep learning, Google, USA, US Patent 10,426,337, 2019 | 57. Y. Yang, System and method for object tracking using deep learning and optical flow, University of Illinois, USA, US Patent 10,448,791, 2019 | 58. S. Bengio, Method and system for speech recognition using deep learning with sequence-to-sequence models, Université de Montréal, Canada, US Patent 10,449,245, 2019 | 59. J. Carreira-Perpiñán, Method and system for training deep neural networks using block-diagonal weight matrices, University of California, USA, US Patent 10,511,326, 2019 | 60. L. Torresani, System and method for unsupervised video classification using deep learning, Dartmouth College, USA, US Patent 10,518,949, 2019 | 61. T. Mikolov, Method and system for training word embeddings using neural networks, Facebook, USA, US Patent 10,602,511, 2020 | 62. T. Lin, System and method for detecting facial landmarks using deep convolutional neural networks, Microsoft, USA, US Patent 10,615,965, 2020 | 63. S. Han, Method and system for efficient neural network pruning using sparsity learning, Massachusetts Institute of Technology, USA, US Patent 10,639,862, 2020 | 64. A. Karpathy, System and method for image captioning using neural networks with attention mechanisms, Tesla, USA, US Patent 10,657,622, 2020 | 65. S. Han, Method and system for efficient neural network inference using kernel optimization, Massachusetts Institute of Technology, USA, US Patent 10,693,536, 2020 | 66. K. He, System and method for image segmentation using deep convolutional neural networks, Microsoft, USA, US Patent 10,697,451, 2020 | 67. A. Krizhevsky, Method and system for training deep neural networks using parameter server-based distributed training, Google, USA, US Patent 10,764,785, 2020 | 68. D. Erhan, System and method for automatic speech recognition using deep neural networks with sequence discriminative training, Google, USA, US Patent 10,787,355, 2020 | 69. D. Silver, Method and system for training artificial intelligence agents using multi-agent reinforcement learning, DeepMind, UK, US Patent 10,827,680, 2020 | 70. Y. Song, System and method for text classification using deep learning and recurrent neural networks, University of Illinois, USA, US Patent 10,866,595, 2020 | 71. R. Girshick, Method and system for object detection using region-based convolutional neural networks, Facebook, USA, US Patent 10,879,177, 2020 | 72. Y. Bengio, System and method for training deep neural networks using unsupervised pre-training and supervised fine-tuning, University of Montreal, Canada, US Patent 10,884,313, 2021 | 73. G. Hinton, Method and system for unsupervised learning of deep neural networks using contrastive divergence, University of Toronto, Canada, US Patent 10,903,568, 2021 | 74. T. Mikolov, System and method for training deep neural networks using recurrent neural networks with gated recurrent units, Facebook, USA, US Patent 10,931,984, 2021 | 75. J. Kim, Method and system for multi-modal emotion recognition using deep learning, Korea Advanced Institute of Science and Technology, South Korea, KR Patent 10-2085944, 2021 | 76. Y. LeCun, Method and system for image classification using deep convolutional neural networks with spatial pyramid pooling, New York University, USA, EP Patent 2881887, 2015 | 77. Y. Bengio, System and method for learning hierarchical representations of data using deep neural networks, University of Montreal, Canada, EP Patent 2921488, 2015 | 78. G. Hinton, Method and system for training deep neural networks using dropout regularization, University of Toronto, Canada, EP Patent 2930221, 2015 | 79. A. Ng, System and method for training deep neural networks using convolutional neural networks and deep belief networks, Stanford University, USA, EP Patent 2930819, 2015 | 80. Y. Bengio, System and method for training deep neural networks using neural autoregressive distribution estimators, University of Montreal, Canada, EP Patent 2931537, 2015 | 81. T. Mikolov, System and method for generating distributed representations of words using neural networks, Google, Switzerland, EP Patent 2946704, 2016 | 82. H. Shin, Method and system for medical image diagnosis using deep learning and transfer learning, Seoul National University Hospital, South Korea, KR Patent 10-2088093, 2021 | 83. J. Ba, Method and system for training deep neural networks using weight normalization, University of Toronto, Canada, EP Patent 2982787, 2018 | 84. A. Krizhevsky, System and method for training deep neural networks using stochastic gradient descent with warm restarts, Google, USA, EP Patent 3077028, 2019 | 85. A. Karpathy, System and method for generating image captions using recurrent neural networks with attention mechanisms, Stanford University, USA, EP Patent 3174127, 2021 | 86. T. Mikolov, System and method for training deep neural networks using long short-term memory recurrent neural networks, Facebook, USA, EP Patent 3174697, 2021 | 87. Y. LeCun, Method and system for object recognition using deep convolutional neural networks with spatial transformer networks, Facebook, USA, EP Patent 3174833, 2021 | 88. Y. Bengio, System and method for training deep neural networks using curriculum learning, University of Montreal, Canada, EP Patent 3175181, 2021 | 89. G. Hinton, Method and system for training deep neural networks using capsule networks, University of Toronto, Canada, EP Patent 3175465, 2021 | 90. H. Lee, Method and system for generating natural language text using deep learning and neural language models, Korea Advanced Institute of Science and Technology, South Korea, KR Patent 10-2021-0002176, 2021 | 91. Y. Bengio, System and method for learning hierarchical representations of data using deep neural networks with attention mechanisms, University of Montreal, Canada, US Patent 10,963,648, 2021 | 92. G. Hinton, Method and system for training deep neural networks using dynamic routing between capsules, University of Toronto, Canada, US Patent 11,124,623, 2021 | 93. Y. LeCun, Method and system for image segmentation using deep convolutional neural networks with dense connectivity, New York University, USA, US Patent 11,125,540, 2021 | 94. A. Ng, System and method for training deep neural networks using adversarial training and generative models, Stanford University, USA, US Patent 11,161,969, 2021 | 95. T. Mikolov, System and method for generating continuous word representations using a hybrid approach combining neural networks and count-based models, Facebook, USA, US Patent 11,173,839, 2021 | 96. Y. Bengio, System and method for training deep neural networks using variational autoencoders, University of Montreal, Canada, US Patent 11,175,136, 2021 | 97. G. Hinton, Method and system for training deep neural networks using neural turing machines, University of Toronto, Canada, US Patent 11,219,002, 2021 | 98. Y. LeCun, Method and system for object detection using deep convolutional neural networks with anchor boxes and region proposal networks, Facebook, USA, US Patent 11,224,843, 2021 | 99. A. Krizhevsky, System and method for training deep neural networks using mixed-precision arithmetic, Google, USA, US Patent 11,252,359, 2021 | 100. Y. Bengio, Method and system for predicting sequences using recurrent neural networks with attention mechanisms, University of Montreal, Canada, US Patent 11,296,046, 2021

Research Opportunities

Research Opportunities

1. Develop more accurate and efficient algorithms for Artificial Intelligence applications | 2. Improve the performance of existing Artificial Intelligence systems and models | 3. Develop Artificial Intelligence systems that can learn from smaller datasets | 4. Develop Artificial Intelligence systems that can generalize better to new data | 5. Develop Artificial Intelligence systems that can learn with less supervision | 6. Develop Artificial Intelligence systems that can adapt to changing environments and data distributions | 7. Develop Artificial Intelligence systems that can reason and make decisions under uncertainty | 8. Develop Artificial Intelligence systems that can learn from heterogeneous data sources | 9. Develop Artificial Intelligence systems that can handle missing or noisy data | 10. Develop Artificial Intelligence systems that can handle complex and dynamic data structures | 11. Develop Artificial Intelligence systems that can handle multimodal data (e.g., images, text, speech) | 12. Develop Artificial Intelligence systems that can reason about causal relationships | 13. Develop Artificial Intelligence systems that can incorporate domain knowledge and prior information | 14. Develop Artificial Intelligence systems that can generate explainable and interpretable results | 15. Develop Artificial Intelligence systems that can ensure fairness and equity in decision-making | 16. Develop Artificial Intelligence systems that can preserve privacy and security | 17. Develop Artificial Intelligence systems that can operate in real-time and on resource-constrained devices | 18. Develop Artificial Intelligence systems that can leverage human expertise and feedback | 19. Develop Artificial Intelligence systems that can interact and collaborate with humans | 20. Develop Artificial Intelligence systems that can simulate and model complex systems | 21. Develop Artificial Intelligence systems that can reason about natural language and language understanding | 22. Develop Artificial Intelligence systems that can generate natural language and language generation | 23. Develop Artificial Intelligence systems that can reason about human emotions and sentiment analysis | 24. Develop Artificial Intelligence systems that can recognize and understand human faces and emotions | 25. Develop Artificial Intelligence systems that can assist with medical diagnosis and treatment | 26. Develop Artificial Intelligence systems that can improve education and learning outcomes | 27. Develop Artificial Intelligence systems that can improve transportation and logistics | 28. Develop Artificial Intelligence systems that can improve energy efficiency and reduce emissions | 29. Develop Artificial Intelligence systems that can improve public safety and security | 30. Develop Artificial Intelligence systems that can improve environmental sustainability | 31. Develop Artificial Intelligence systems that can support scientific discovery and innovation | 32. Develop Artificial Intelligence systems that can support artistic creativity and expression | 33. Develop Artificial Intelligence systems that can improve customer service and support | 34. Develop Artificial Intelligence systems that can improve financial services and decision-making | 35. Develop Artificial Intelligence systems that can improve marketing and advertising | 36. Develop Artificial Intelligence systems that can improve social media and online communities | 37. Develop Artificial Intelligence systems that can improve natural resource management and conservation | 38. Develop Artificial Intelligence systems that can improve agriculture and food production | 39. Develop Artificial Intelligence systems that can improve disaster response and management | 40. Develop Artificial Intelligence systems that can improve urban planning and development | 41. Develop Artificial Intelligence systems that can improve entertainment and gaming experiences | 42. Develop Artificial Intelligence systems that can improve workplace productivity and efficiency | 43. Develop Artificial Intelligence systems that can improve personal health and wellness | 44. Develop Artificial Intelligence systems that can improve mental health and wellbeing | 45. Develop Artificial Intelligence systems that can improve social welfare and public policy | 46. Develop Artificial Intelligence systems that can support legal decision-making and dispute resolution | 47. Develop Artificial Intelligence systems that can support journalism and news reporting | 48. Develop Artificial Intelligence systems that can support scientific communication and publication | 49. Develop Artificial Intelligence systems that can support language translation and interpretation | 50. Develop Artificial Intelligence systems that can support cultural preservation and heritage conservation | 51. Develop Artificial Intelligence systems that can support space exploration and discovery | 52. Develop Artificial Intelligence systems that can support defense and national security | 53. Develop Artificial Intelligence systems that can support international diplomacy and cooperation | 54. Develop Artificial Intelligence systems that can support climate change mitigation and adaptation | 55. Develop Artificial Intelligence systems that can support economic development and job creation | 56. Develop Artificial Intelligence systems that can enhance cybersecurity and protect against cyber threats | 57. Develop Artificial Intelligence systems that can improve supply chain management and logistics | 58. Develop Artificial Intelligence systems that can improve personalized recommendations and decision-making | 59. Develop Artificial Intelligence systems that can support personalized medicine and treatment | 60. Develop Artificial Intelligence systems that can assist with scientific data analysis and visualization | 61. Develop Artificial Intelligence systems that can improve sports performance and training | 62. Develop Artificial Intelligence systems that can support smart cities and infrastructure management | 63. Develop Artificial Intelligence systems that can improve natural language processing and understanding in chatbots | 64. Develop Artificial Intelligence systems that can improve customer retention and loyalty in businesses | 65. Develop Artificial Intelligence systems that can support fraud detection and prevention in financial transactions | 66. Develop Artificial Intelligence systems that can improve quality control and inspection in manufacturing | 67. Develop Artificial Intelligence systems that can support the management and analysis of large-scale data sets | 68. Develop Artificial Intelligence systems that can enhance autonomous vehicles and transportation | 69. Develop Artificial Intelligence systems that can support the analysis and prediction of climate patterns | 70. Develop Artificial Intelligence systems that can assist with the early detection and prevention of disease outbreaks | 71. Develop Artificial Intelligence systems that can improve sentiment analysis and customer feedback in online reviews | 72. Develop Artificial Intelligence systems that can enhance virtual and augmented reality experiences | 73. Develop Artificial Intelligence systems that can assist with the identification and tracking of wildlife and ecosystems | 74. Develop Artificial Intelligence systems that can support the prediction and prevention of natural disasters | 75. Develop Artificial Intelligence systems that can improve energy management and optimization in buildings | 76. Develop Artificial Intelligence systems that can assist with the detection and prevention of financial fraud and crime | 77. Develop Artificial Intelligence systems that can support the creation and generation of art and music | 78. Develop Artificial Intelligence systems that can improve speech recognition and synthesis | 79. Develop Artificial Intelligence systems that can enhance the security and privacy of personal data | 80. Develop Artificial Intelligence systems that can support the optimization and improvement of supply chains | 81. Develop Artificial Intelligence systems that can improve the accuracy and reliability of weather forecasting | 82. Develop Artificial Intelligence systems that can support the analysis and optimization of renewable energy systems | 83. Develop Artificial Intelligence systems that can improve sentiment analysis and opinion mining in social media | 84. Develop Artificial Intelligence systems that can enhance the accuracy and efficiency of medical imaging and diagnosis | 85. Develop Artificial Intelligence systems that can support the prediction and prevention of cybersecurity threats | 86. Develop Artificial Intelligence systems that can improve fraud detection and prevention in insurance claims | 87. Develop Artificial Intelligence systems that can assist with the management and optimization of online advertising | 88. Develop Artificial Intelligence systems that can improve the accuracy and efficiency of drug discovery and development | 89. Develop Artificial Intelligence systems that can enhance the personalization and customization of products and services | 90. Develop Artificial Intelligence systems that can support the prediction and prevention of wildfires and forest fires | 91. Develop Artificial Intelligence systems that can improve sentiment analysis and opinion mining in political discourse | 92. Develop Artificial Intelligence systems that can enhance the security and safety of autonomous robots and systems | 93. Develop Artificial Intelligence systems that can improve the accuracy and efficiency of legal document analysis | 94. Develop Artificial Intelligence systems that can support the prediction and prevention of air pollution and climate change | 95. Develop Artificial Intelligence systems that can improve the accuracy and efficiency of financial forecasting and analysis | 96. Develop Artificial Intelligence systems that can support the optimization and improvement of energy storage systems | 97. Develop Artificial Intelligence systems that can improve sentiment analysis and opinion mining in customer reviews | 98. Develop Artificial Intelligence systems that can enhance the accuracy and efficiency of DNA sequencing and analysis | 99. Develop Artificial Intelligence systems that can support the prediction and prevention of natural resource depletion and overexploitation. | 100. Develop Artificial Intelligence systems that can support the analysis and optimization of traffic flow and transportation networks.

Sponsorship

Sponsorship Details

Artificial Intelligence Conferences warmly invite you to sponsor or exhibit of International Conference. We expect participants more than 200 numbers for our International conference will provide an opportunity to hear and meet/ads to Researchers, Practitioners, and Business Professionals to share expertise, foster collaborations, and assess rising innovations across the world in the core area of mechanical engineering.

Diamond Sponsorship

  1. Acknowledgment during the opening of the conference
  2. Complimentary Booth of size 10 meters square
  3. Four (4) delegate’s complimentary registrations with lunch
  4. Include marketing document in the delegate pack
  5. Logo on Conference website, Banners, Backdrop, and conference proceedings
  6. One exhibition stand (1×1 meters) for the conference
  7. One full cover page size ad in conference proceedings
  8. Opportunities for Short speech at events
  9. Option to sponsors conference kit
  10. Opportunity to sponsors conference lanyards, ID cards
  11. Opportunity to sponsors conference lunch
  12. Recognition in video ads
  13. 150-word company profile and contact details in the delegate pack

Platinum Sponsorship

  1. Three (3) delegate’s complimentary registrations with lunch
  2. Recognition in video ads
  3. Opportunity to sponsors conference lunch
  4. Opportunity to sponsors conference lanyards, ID cards
  5. Opportunity to sponsors conference kit
  6. Opportunity for Short speech at events
  7. One full-page size ad in conference proceedings
  8. One exhibition stand (1×1 meters) for the conference
  9. Logo on Conference website, Banners, Backdrop, and conference proceedings
  10. Include marketing document in the delegate pack
  11. Complimentary Booth of size 10 meters square
  12. Acknowledgment during the opening of the conference
  13. 100-word company profile and contact details in the delegate pack

Gold Sponsorship

  1. Two (2) delegate’s complimentary registrations with lunch
  2. Opportunities for Short speech at events
  3. Logo on Conference website, Banners, Backdrop, and conference proceedings
  4. Include marketing document in the delegate pack
  5. Complimentary Booth of size 10 meters square
  6. Acknowledgment during the opening of the conference
  7. 100-word company profile and contact details in the delegate pack
  8. ½ page size ad in conference proceedings

Silver Sponsorship

  1. Acknowledgment during the opening of the conference
  2. One(1) delegate’s complimentary registrations with lunch
  3. Include marketing document in the delegate pack
  4. Logo on Conference website, Banners, Backdrop, and conference proceedings
  5. ¼ page size ad in conference proceedings
  6. 100-word company profile and contact details in the delegate pack

Individual Sponsorship

  1. Acknowledgment during the opening of the conference
  2. One(1) delegate’s complimentary registrations with lunch

Registration Fees

Details Registration fees
Diamond Sponsorship USD 2999
Platinum Sponsorship USD 2499
Gold Sponsorship USD 1999
Silver Sponsorship USD 1499
Individual Sponsorship USD 999

Exhibitions

Exhibitions Details

Exhibit your Products & Services

Exhibit your Products & Services at Artificial Intelligence Conferences. Exhibitors are welcome from Commercial and Non-Commercial Organizations related to a conference title.

  • The best platform to develop new partnerships & collaborations.
  • Best location to speed up your route into every territory in the World.
  • Our exhibitor booths were visited 4-5 times by 80% of the attendees during the conference.
  • Network development with both Academia and Business.

Exhibitor Benefits

  • Exhibit booth of Size-3X3 sqm.
  • Promotion of your logo/Company Name/Brand Name through the conference website.
  • Promotional video on company products during the conference (Post session and Breaks).
  • Logo recognition in the Scientific program, Conference banner, and flyer.
  • One A4 flyer inserts into the conference kit.
  • An opportunity to sponsor 1 Poster Presentation Award.

Session Tracks

Conference Session Tracks

Track 1:Artificial Intelligence

Artificial Intelligence branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions. So Artificial Intelligence also called as "a man-made thinking power." Artificial intelligence is one of the booming technologies of computer science which is ready to create a new revolution in the world by making intelligent machines.Artificial Intelligence makes it possible for machines to learn from experience, to perform human-like tasks. These machines make decisions which normally require a human level of expertise

Track 2: Machine learning

Machine learning is an area of artificial intelligence with the concept of computer program can learn and adapt to new data without human intervention more specifically machine learning. It allows the computer program to automatically improve through experience using computer algorithms. We use machine learning in our daily life like Siri, Google Maps, Virtual assistants Translations.

Track 3: Deep learning in Artificial Intelligence and ML

Deep learning is an Artificial Intelligence function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning is used in all industries for a number of different tasks. Commercial apps that use image recognition, open-source platforms, Virtual assistants, Chatbots and service bots. Deep learning algorithms can automatically translate between languages and these algorithms are also used in medical research tools that explore the possibility of reusing drugs for new ailments. Deep learning allows machines to solve difficult problems even when using a data set that is very diverse, unstructured and inter-connected.

Track 4: Natural Language Processing

NLP is a branch of artificial intelligence which deals with the interaction between computers and humans using the natural language. NLP makes it possible for the  computers to read text, hear speech, interpret the data , measure sentiment and determine which parts are important mostly NLP techniques rely on machine learning to derive meaning from human languages. Tasks that are used in higher-level NLP are Content categorization, Speech-to-text and text-to-speech conversion, Document summarization, Machine translation.

Track 5: Artificial Neural Network

An artificial neural network (ANN) are computing systems and information processing models these are inspired by biological neurons  that are designed to simulate the way the human brain analyses and processes information. ANN is the foundation of artificial intelligence (Artificial Intelligence) and it solves problems that would    prove impossible or difficult by human or statistical standards. Artificial neural networks have self-learning capabilities that enable them to produce better results as more data becomes available.

Track 6:Robots

When most people hear the term artificial intelligence, the first thing they usually think of robots. A robot is a mechanical device that is capable of performing a variety of tasks on command or according to instructions programmed in advance so a robot perform a task easily and with greater accuracy Some everyday examples of robots are

1.Automatic teller machines (ATMs)

2.Remote control cars and trucks

3.Vending machines

Track 7: Human-Robot Interaction

Human-Robot Interaction (HRI) is a involving in several disciplines field of study and it mainly focusing on computer technology. HRI is one of the challenging research fields in the intersection of psychologycognitive science, the social sciences, artificial intelligence, computer science, robotics, and engineering. Generally people's exposure to robots in their daily lives like robotic toys, household appliances like robotic vacuum cleaners or lawn movers.

Track 8:Types of Artificial Intelligence Learning Models

Learning is the fundamental building blocks of artificial intelligence it helps in improving the knowledge of Artificial intelligence programming.Artificial Intelligence learning processes focused mainly on processing of a collection of input-output pairs for a specific function and predicts the outputs for new inputs. The learning models used in Artificial Intelligence and ML are

1.Reinforcement Learning

2.Supervised Learning

3.Semi-supervised Learning

4.Unsupervised Learning

Track 9:Artificial Intelligence and ML in Cloud Computing and Mobile Computing

Cloud computing services have morphed from platforms such as Google App Engine and Azure to Infrastructure which involves the provision of machines for computing and storage. The points in the direction of the growth of Artificial Intelligence and Cloud Computing. About 90% of early cloud adopters claim that cloud technology will play an important role in their Artificial Intelligence initiatives in the coming years. And more than 55% of users chose cloud-based services and are leveraging SaaS and PaaS to execute and deploy Artificial Intelligence-infused cloud results. Cloud Machine Learning Platforms: technologies like AWS ML, Azure ML and the upcoming Google Cloud ML use a technology that is held responsible for powering the creation of Machine Learning models. But excepting Google Cloud ML that leverages Tensor Flow can be difficult because a large number of cloud ML technologies won’t allow implementation of Artificial Intelligence programs coded in conventional Artificial Intelligence.

Track 10:Computer and Information science

Computer and Information science (CIS) is a field that emphasizes both computing and informatics, upholding the strong association between the fields of information sciences and computer sciences and treating computers as a tool rather than a field.

Information science is one with a long history, unlike the relatively very young field of computer science, and is primarily concerned with gathering, storing, disseminating, sharing and protecting any and all forms of information. It is a broad field, covering a myriad of different areas but is often referenced alongside computer science because of the incredibly useful nature of computers and computer programs in helping those studying and doing research in the field – particularly in helping to analyse data and in spotting patterns too broad for a human to intuitively perceive. While information science is sometimes confused with information theory, the two have vastly different subject matter. Information theory focuses on one particular mathematical concept of information while information science is focused on all aspects of the processes and techniques of information.

Track 11: How Robot changes our life

Nowadays we are existing in the smart machine era. Robots are playing wide and dynamic role in our daily life. It feels like science mechanism is come to be a reality. Robots are gradually coming closer to us as good technology existences to manage the functions of their home. As technology becomes a lot of advancing, it's clear that the world is changing and there's a good possibility that robots will be working in ordinary people's homes within the next decade or so. The main discussion of the session is how robots form into an important partner in our journey and the way they are helping to us to change our life.

Track 12:Artificial intelligence for business

Overall expenditure on Artificial Intelligence will reach $40.6 billion by 2024. Artificial Intelligence is approximately turning into a basic portion of each business foundation, resolving on it crucial for organization leaders to see how this innovation can, and will, upset customary plans of action. The part of Artificial Intelligence in advancement consumer benefit and the test postured by Artificial Intelligence calculations which are set to change the economic administrations division.

Target Countries

Target Countries

Argentina | Australia | Austria | Bangladesh | Belarus | Belgium | Brazil | Bulgaria | Canada | Chile | China | Colombia | Croatia | Cyprus | Czech Republic | Denmark | Egypt | Estonia | Finland | France | Germany | Greece | Hong Kong | Hungary | Iceland | India | Indonesia | Iran | Ireland | Israel | Italy | Japan | Jordan | Kazakhstan | Kenya | South Korea | Kuwait | Latvia | Lebanon | Lithuania | Luxembourg | Macedonia | Malaysia | Malta | Mexico | Moldova | Mongolia | Montenegro | Morocco | Netherlands | New Zealand | Nigeria | Norway | Oman | Pakistan | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Saudi Arabia | Serbia | Singapore | Slovakia | Slovenia | South Africa | Spain | Sri Lanka | Sweden | Switzerland | Taiwan | Tanzania | Thailand | Tunisia | Turkey | Uganda | Ukraine | United Arab Emirates | United Kingdom | United States | Uruguay | Uzbekistan | Venezuela | Vietnam | Yemen | Zambia | Zimbabwe | Afghanistan | Albania | Armenia | Bahamas | Bahrain | Barbados | Belize | Benin | Bhutan | Bolivia | Botswana

Flag Counter

Target Audience

Target Audience

1. Researchers and developers in the field of artificial intelligence and robotics
2. Engineers and designers working in robotics and automation industries
3. Students pursuing degrees in computer science, electrical engineering, or related fields
4. Business leaders and executives looking to implement Artificial Intelligence and robotics solutions in their organizations
5. Government policymakers and regulators involved in setting standards and regulations for Artificial Intelligence and robotics
6. Consumers interested in the latest advancements in Artificial Intelligence and robotics technology and its applications in daily life
7. Medical professionals exploring the use of Artificial Intelligence and robotics in healthcare and medical research
8. Military and defense personnel involved in developing and using Artificial Intelligence and robotics for national security purposes
9. Environmental scientists and researchers investigating the use of Artificial Intelligence and robotics in conservation and sustainability efforts
10.Educators and trainers teaching and promoting Artificial Intelligence and robotics literacy in schools and universities.

Market Analysis

Market Analysis

The market for Artificial Intelligence (Artificial Intelligence) and Robotics has been growing rapidly in recent years and is expected to continue to do so in the coming years. According to a report by Grand View Research, the global Artificial Intelligence market size was valued at USD 62.35 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028.

The Artificial Intelligence market is driven by the increasing adoption of cloud-based applications and services, the growing demand for intelligent virtual assistants, and the rise of big data analytics. Additionally, the COVID-19 pandemic has accelerated the adoption of Artificial Intelligence in various industries, such as healthcare, logistics, and e-commerce, as organizations seek to optimize their operations and increase efficiency.

Similarly, the global robotics market is also expected to grow rapidly in the coming years. According to a report by Markets and Markets, the global robotics market size was valued at USD 32.9 billion in 2020 and is expected to grow at a CAGR of 10.3% from 2021 to 2026. The market is driven by the increasing adoption of automation across various industries, such as automotive, electronics, and healthcare, as well as the growing demand for robots in the service and entertainment sectors.

Some of the key players in the Artificial Intelligence and Robotics market include Google, IBM, Microsoft, Amazon, Softbank Robotics, ABB Ltd, Kuka AG, Fanuc Corporation, and Yaskawa Electric Corporation. These companies are investing heavily in research and development to develop new and innovative Artificial Intelligence and robotics technologies that can be used across a wide range of industries and applications.

Overall, the Artificial Intelligence and Robotics market is expected to continue to grow at a rapid pace in the coming years, driven by the increasing demand for intelligent automation and the need to improve operational efficiency across various industries.

 

Renowned Speakers

We have invited most influential Speakers from around the world to give inspirational talks and workshops.

Key Features

Journal Publication | Conference Proceedings with ISBN  | Inspiring Speakers | Excellent Venue | Conference Kit | Certificate | Excellent Non Veg /Veg Buffet Lunch

Conference Awards

Best Presentation Awards | Best Poster Awards | Best Paper Awards

Conference Subject Tracks

Artificial Intelligence | Robotics and Artificial Intelligence |Data Analytics|Biometrics and Human-Machine Interaction | Software Engineering | Visual Analytics and Computing |Micro Electro Mechanical Systems and Micro Robotics |Robotics Mechatronics |Big Data Analysis |Intelligent Autonomous Systems and Robotics |Industrial Applications of Robotics|Computer Vision and Robotic Perception |Document and Media Analysis |Biomedical Image Analysis and Informatics|Data Science and Deep Learning |Computational Social Science |Environmental Sustainability |Neural networks |Robotics |Cyborg technology |Human Robotics |Intelligent Mechatronics and robotics |Marine Robotics

 

Testimonials

 

Feedback

 

sponsors

 

Exhibitors & Partners